SVM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "087b512e-dc27-4661-89c5-b383eb66ad05",
   "metadata": {},
   "source": [
    "# Forecasting US Interest Rate: By Support Vector Regression(SVR) Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b04cc65f-70c1-42c6-ad4d-f9d28153b50f",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fafc19a7-903d-4c12-b8bf-71af7154818e",
   "metadata": {},
   "source": [
    "Support Vector Regression (SVR) is a supervised machine learning algorithm derived from Support Vector Machines (SVM). While SVM is primarily designed for classification tasks, SVR adapts the principles of SVM to predict continuous outcomes, offering a robust and flexible approach to regression problems. It is especially popular in cases where the data contains noise, and the relationship between input and output variables is not strictly linear.\n",
    "\n",
    "The core idea of SVR is to find a function that approximates the relationship between input features and output values while maintaining a margin of tolerance (epsilon, ε) around the predicted value. Unlike traditional regression models, SVR focuses on minimizing the error within this margin rather than minimizing the overall error, which makes it robust to outliers and noise in the data.\n",
    "\n",
    "SVR is a powerful and versatile tool for regression tasks, offering strong generalization, robustness to noise, and the ability to handle both linear and non-linear relationships. Its flexibility and adaptability make it a popular choice in applications ranging from financial forecasting to engineering and healthcare. However, its computational complexity and sensitivity to hyperparameters require careful tuning and consideration for large datasets.In this chapter, we employ the SVR model to predict the Federal Funds Rate (FEDFUNDS) as part of achieving the project's objectives. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "85ef1ce6-8b5c-4a70-9d65-f6b5c882fd4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standard libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.dates as mdates\n",
    "from pandas import read_csv\n",
    "import time\n",
    "sns.set(style='whitegrid', palette='muted')\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 10, 6\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import uniform\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a03611f6-4fea-4de4-8d67-72c9d2d2a095",
   "metadata": {},
   "source": [
    "## Data Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d59445d-92df-439e-966c-9ee682421ace",
   "metadata": {},
   "source": [
    "The data preprocessing section was mainly adapted from the code provided by Professor C-Y (Eric) NG in the Econ 5060 course. Macroeconomic data were preprocessed to address missing values, transform variables, standardize features, and construct lagged features to capture temporal dependencies and patterns within the time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "597db3bd-c2d9-42e1-bfe6-bf72c14d2305",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(773, 127)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro = pd.read_csv(r'D:\\HuaweiMoveData\\Users\\WRY\\Desktop\\US FRED-MD Macro Dataset.csv', header=0, index_col=0, parse_dates=True)\n",
    "macro.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "de72fbd0-62e5-4b08-a7b2-861e1f59c744",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPI</th>\n",
       "      <th>W875RX1</th>\n",
       "      <th>DPCERA3M086SBEA</th>\n",
       "      <th>CMRMTSPLx</th>\n",
       "      <th>RETAILx</th>\n",
       "      <th>INDPRO</th>\n",
       "      <th>IPFPNSS</th>\n",
       "      <th>IPFINAL</th>\n",
       "      <th>IPCONGD</th>\n",
       "      <th>IPDCONGD</th>\n",
       "      <th>...</th>\n",
       "      <th>DNDGRG3M086SBEA</th>\n",
       "      <th>DSERRG3M086SBEA</th>\n",
       "      <th>CES0600000008</th>\n",
       "      <th>CES2000000008</th>\n",
       "      <th>CES3000000008</th>\n",
       "      <th>UMCSENTx</th>\n",
       "      <th>DTCOLNVHFNM</th>\n",
       "      <th>DTCTHFNM</th>\n",
       "      <th>INVEST</th>\n",
       "      <th>VIXCLSx</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sasdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>2442.158</td>\n",
       "      <td>2293.2</td>\n",
       "      <td>17.272</td>\n",
       "      <td>2.922664e+05</td>\n",
       "      <td>18235.77392</td>\n",
       "      <td>21.9665</td>\n",
       "      <td>23.3891</td>\n",
       "      <td>22.2688</td>\n",
       "      <td>31.7011</td>\n",
       "      <td>19.0149</td>\n",
       "      <td>...</td>\n",
       "      <td>17.791</td>\n",
       "      <td>11.326</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6476.00</td>\n",
       "      <td>12298.00</td>\n",
       "      <td>84.2043</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-02</th>\n",
       "      <td>2451.778</td>\n",
       "      <td>2301.5</td>\n",
       "      <td>17.452</td>\n",
       "      <td>2.944247e+05</td>\n",
       "      <td>18369.56308</td>\n",
       "      <td>22.3966</td>\n",
       "      <td>23.7048</td>\n",
       "      <td>22.4617</td>\n",
       "      <td>31.9337</td>\n",
       "      <td>19.1147</td>\n",
       "      <td>...</td>\n",
       "      <td>17.798</td>\n",
       "      <td>11.343</td>\n",
       "      <td>2.14</td>\n",
       "      <td>2.46</td>\n",
       "      <td>2.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6476.00</td>\n",
       "      <td>12298.00</td>\n",
       "      <td>83.5280</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-03</th>\n",
       "      <td>2467.594</td>\n",
       "      <td>2318.5</td>\n",
       "      <td>17.617</td>\n",
       "      <td>2.934187e+05</td>\n",
       "      <td>18523.05762</td>\n",
       "      <td>22.7193</td>\n",
       "      <td>23.8483</td>\n",
       "      <td>22.5719</td>\n",
       "      <td>31.9337</td>\n",
       "      <td>19.4890</td>\n",
       "      <td>...</td>\n",
       "      <td>17.785</td>\n",
       "      <td>11.363</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6508.00</td>\n",
       "      <td>12349.00</td>\n",
       "      <td>81.6405</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-04</th>\n",
       "      <td>2483.671</td>\n",
       "      <td>2334.9</td>\n",
       "      <td>17.553</td>\n",
       "      <td>2.993228e+05</td>\n",
       "      <td>18534.46600</td>\n",
       "      <td>23.2032</td>\n",
       "      <td>24.1927</td>\n",
       "      <td>22.9026</td>\n",
       "      <td>32.4374</td>\n",
       "      <td>19.6138</td>\n",
       "      <td>...</td>\n",
       "      <td>17.796</td>\n",
       "      <td>11.403</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6620.00</td>\n",
       "      <td>12484.00</td>\n",
       "      <td>81.8099</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-05</th>\n",
       "      <td>2498.026</td>\n",
       "      <td>2350.4</td>\n",
       "      <td>17.765</td>\n",
       "      <td>3.013643e+05</td>\n",
       "      <td>18679.66354</td>\n",
       "      <td>23.5528</td>\n",
       "      <td>24.3936</td>\n",
       "      <td>23.1231</td>\n",
       "      <td>32.5925</td>\n",
       "      <td>20.0130</td>\n",
       "      <td>...</td>\n",
       "      <td>17.777</td>\n",
       "      <td>11.421</td>\n",
       "      <td>2.17</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.08</td>\n",
       "      <td>95.3</td>\n",
       "      <td>6753.00</td>\n",
       "      <td>12646.00</td>\n",
       "      <td>80.7315</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>17821.000</td>\n",
       "      <td>14623.3</td>\n",
       "      <td>130.028</td>\n",
       "      <td>1.581882e+06</td>\n",
       "      <td>692501.00000</td>\n",
       "      <td>102.5080</td>\n",
       "      <td>101.2863</td>\n",
       "      <td>101.3295</td>\n",
       "      <td>102.0755</td>\n",
       "      <td>104.8646</td>\n",
       "      <td>...</td>\n",
       "      <td>114.885</td>\n",
       "      <td>134.903</td>\n",
       "      <td>28.91</td>\n",
       "      <td>33.44</td>\n",
       "      <td>25.82</td>\n",
       "      <td>64.9</td>\n",
       "      <td>383384.74</td>\n",
       "      <td>720635.04</td>\n",
       "      <td>5505.6964</td>\n",
       "      <td>20.2269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-02</th>\n",
       "      <td>17839.528</td>\n",
       "      <td>14622.7</td>\n",
       "      <td>129.991</td>\n",
       "      <td>1.575290e+06</td>\n",
       "      <td>687942.00000</td>\n",
       "      <td>102.5023</td>\n",
       "      <td>101.0873</td>\n",
       "      <td>101.1983</td>\n",
       "      <td>102.1753</td>\n",
       "      <td>105.0877</td>\n",
       "      <td>...</td>\n",
       "      <td>115.279</td>\n",
       "      <td>135.397</td>\n",
       "      <td>28.98</td>\n",
       "      <td>33.61</td>\n",
       "      <td>25.81</td>\n",
       "      <td>67.0</td>\n",
       "      <td>385165.01</td>\n",
       "      <td>718917.35</td>\n",
       "      <td>5491.3981</td>\n",
       "      <td>20.2141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-03</th>\n",
       "      <td>17885.887</td>\n",
       "      <td>14664.3</td>\n",
       "      <td>129.948</td>\n",
       "      <td>1.560472e+06</td>\n",
       "      <td>681673.00000</td>\n",
       "      <td>102.6521</td>\n",
       "      <td>101.0102</td>\n",
       "      <td>101.1678</td>\n",
       "      <td>102.4580</td>\n",
       "      <td>103.9786</td>\n",
       "      <td>...</td>\n",
       "      <td>114.930</td>\n",
       "      <td>135.782</td>\n",
       "      <td>29.12</td>\n",
       "      <td>33.79</td>\n",
       "      <td>25.95</td>\n",
       "      <td>62.0</td>\n",
       "      <td>388912.72</td>\n",
       "      <td>719670.11</td>\n",
       "      <td>5366.2449</td>\n",
       "      <td>22.0446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-04</th>\n",
       "      <td>17866.773</td>\n",
       "      <td>14659.0</td>\n",
       "      <td>130.255</td>\n",
       "      <td>1.556928e+06</td>\n",
       "      <td>684213.00000</td>\n",
       "      <td>103.1748</td>\n",
       "      <td>101.7373</td>\n",
       "      <td>102.1572</td>\n",
       "      <td>103.4504</td>\n",
       "      <td>108.3743</td>\n",
       "      <td>...</td>\n",
       "      <td>115.341</td>\n",
       "      <td>136.315</td>\n",
       "      <td>29.22</td>\n",
       "      <td>33.92</td>\n",
       "      <td>26.03</td>\n",
       "      <td>63.5</td>\n",
       "      <td>390133.21</td>\n",
       "      <td>720155.25</td>\n",
       "      <td>5250.7099</td>\n",
       "      <td>17.4930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-05</th>\n",
       "      <td>17915.688</td>\n",
       "      <td>14702.7</td>\n",
       "      <td>130.222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>686573.00000</td>\n",
       "      <td>103.0107</td>\n",
       "      <td>101.6856</td>\n",
       "      <td>102.0669</td>\n",
       "      <td>103.3327</td>\n",
       "      <td>108.7279</td>\n",
       "      <td>...</td>\n",
       "      <td>114.944</td>\n",
       "      <td>136.665</td>\n",
       "      <td>29.36</td>\n",
       "      <td>34.07</td>\n",
       "      <td>26.19</td>\n",
       "      <td>59.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5220.8945</td>\n",
       "      <td>17.3964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>773 rows × 127 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  RPI  W875RX1  DPCERA3M086SBEA     CMRMTSPLx       RETAILx  \\\n",
       "sasdate                                                                       \n",
       "1959-01-01   2442.158   2293.2           17.272  2.922664e+05   18235.77392   \n",
       "1959-01-02   2451.778   2301.5           17.452  2.944247e+05   18369.56308   \n",
       "1959-01-03   2467.594   2318.5           17.617  2.934187e+05   18523.05762   \n",
       "1959-01-04   2483.671   2334.9           17.553  2.993228e+05   18534.46600   \n",
       "1959-01-05   2498.026   2350.4           17.765  3.013643e+05   18679.66354   \n",
       "...               ...      ...              ...           ...           ...   \n",
       "2023-01-01  17821.000  14623.3          130.028  1.581882e+06  692501.00000   \n",
       "2023-01-02  17839.528  14622.7          129.991  1.575290e+06  687942.00000   \n",
       "2023-01-03  17885.887  14664.3          129.948  1.560472e+06  681673.00000   \n",
       "2023-01-04  17866.773  14659.0          130.255  1.556928e+06  684213.00000   \n",
       "2023-01-05  17915.688  14702.7          130.222           NaN  686573.00000   \n",
       "\n",
       "              INDPRO   IPFPNSS   IPFINAL   IPCONGD  IPDCONGD  ...  \\\n",
       "sasdate                                                       ...   \n",
       "1959-01-01   21.9665   23.3891   22.2688   31.7011   19.0149  ...   \n",
       "1959-01-02   22.3966   23.7048   22.4617   31.9337   19.1147  ...   \n",
       "1959-01-03   22.7193   23.8483   22.5719   31.9337   19.4890  ...   \n",
       "1959-01-04   23.2032   24.1927   22.9026   32.4374   19.6138  ...   \n",
       "1959-01-05   23.5528   24.3936   23.1231   32.5925   20.0130  ...   \n",
       "...              ...       ...       ...       ...       ...  ...   \n",
       "2023-01-01  102.5080  101.2863  101.3295  102.0755  104.8646  ...   \n",
       "2023-01-02  102.5023  101.0873  101.1983  102.1753  105.0877  ...   \n",
       "2023-01-03  102.6521  101.0102  101.1678  102.4580  103.9786  ...   \n",
       "2023-01-04  103.1748  101.7373  102.1572  103.4504  108.3743  ...   \n",
       "2023-01-05  103.0107  101.6856  102.0669  103.3327  108.7279  ...   \n",
       "\n",
       "            DNDGRG3M086SBEA  DSERRG3M086SBEA  CES0600000008  CES2000000008  \\\n",
       "sasdate                                                                      \n",
       "1959-01-01           17.791           11.326           2.13           2.45   \n",
       "1959-01-02           17.798           11.343           2.14           2.46   \n",
       "1959-01-03           17.785           11.363           2.15           2.45   \n",
       "1959-01-04           17.796           11.403           2.16           2.47   \n",
       "1959-01-05           17.777           11.421           2.17           2.48   \n",
       "...                     ...              ...            ...            ...   \n",
       "2023-01-01          114.885          134.903          28.91          33.44   \n",
       "2023-01-02          115.279          135.397          28.98          33.61   \n",
       "2023-01-03          114.930          135.782          29.12          33.79   \n",
       "2023-01-04          115.341          136.315          29.22          33.92   \n",
       "2023-01-05          114.944          136.665          29.36          34.07   \n",
       "\n",
       "            CES3000000008  UMCSENTx  DTCOLNVHFNM   DTCTHFNM     INVEST  \\\n",
       "sasdate                                                                  \n",
       "1959-01-01           2.04       NaN      6476.00   12298.00    84.2043   \n",
       "1959-01-02           2.05       NaN      6476.00   12298.00    83.5280   \n",
       "1959-01-03           2.07       NaN      6508.00   12349.00    81.6405   \n",
       "1959-01-04           2.08       NaN      6620.00   12484.00    81.8099   \n",
       "1959-01-05           2.08      95.3      6753.00   12646.00    80.7315   \n",
       "...                   ...       ...          ...        ...        ...   \n",
       "2023-01-01          25.82      64.9    383384.74  720635.04  5505.6964   \n",
       "2023-01-02          25.81      67.0    385165.01  718917.35  5491.3981   \n",
       "2023-01-03          25.95      62.0    388912.72  719670.11  5366.2449   \n",
       "2023-01-04          26.03      63.5    390133.21  720155.25  5250.7099   \n",
       "2023-01-05          26.19      59.2          NaN        NaN  5220.8945   \n",
       "\n",
       "            VIXCLSx  \n",
       "sasdate              \n",
       "1959-01-01      NaN  \n",
       "1959-01-02      NaN  \n",
       "1959-01-03      NaN  \n",
       "1959-01-04      NaN  \n",
       "1959-01-05      NaN  \n",
       "...             ...  \n",
       "2023-01-01  20.2269  \n",
       "2023-01-02  20.2141  \n",
       "2023-01-03  22.0446  \n",
       "2023-01-04  17.4930  \n",
       "2023-01-05  17.3964  \n",
       "\n",
       "[773 rows x 127 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9015a099-0d13-4280-9032-a57ba968431c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 127)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imports data\n",
    "tran_code = pd.read_csv(r'D:\\HuaweiMoveData\\Users\\WRY\\Desktop\\US FRED-MD Macro code trans.csv', header=0, index_col=0, parse_dates=False)\n",
    "tran_code.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1dfadfca-0558-48db-b728-ddb5cf493adf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPI</th>\n",
       "      <th>W875RX1</th>\n",
       "      <th>DPCERA3M086SBEA</th>\n",
       "      <th>CMRMTSPLx</th>\n",
       "      <th>RETAILx</th>\n",
       "      <th>INDPRO</th>\n",
       "      <th>IPFPNSS</th>\n",
       "      <th>IPFINAL</th>\n",
       "      <th>IPCONGD</th>\n",
       "      <th>IPDCONGD</th>\n",
       "      <th>...</th>\n",
       "      <th>DNDGRG3M086SBEA</th>\n",
       "      <th>DSERRG3M086SBEA</th>\n",
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       "      <th>CES3000000008</th>\n",
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       "      <th>DTCTHFNM</th>\n",
       "      <th>INVEST</th>\n",
       "      <th>VIXCLSx</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tran_code</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "<p>1 rows × 127 columns</p>\n",
       "</div>"
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       "           RPI  W875RX1  DPCERA3M086SBEA  CMRMTSPLx  RETAILx  INDPRO  IPFPNSS  \\\n",
       "Symbol                                                                          \n",
       "Tran_code    5        5                5          5        5       5        5   \n",
       "\n",
       "           IPFINAL  IPCONGD  IPDCONGD  ...  DNDGRG3M086SBEA  DSERRG3M086SBEA  \\\n",
       "Symbol                                 ...                                     \n",
       "Tran_code        5        5         5  ...                6                6   \n",
       "\n",
       "           CES0600000008  CES2000000008  CES3000000008  UMCSENTx  DTCOLNVHFNM  \\\n",
       "Symbol                                                                          \n",
       "Tran_code              6              6              6         2            6   \n",
       "\n",
       "           DTCTHFNM  INVEST  VIXCLSx  \n",
       "Symbol                                \n",
       "Tran_code         6       6        1  \n",
       "\n",
       "[1 rows x 127 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tran_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1982f93f-ff80-42fc-83f7-7d8d1f2380a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_transform(x, tran_code):\n",
    "    \n",
    "    if tran_code == 1:\n",
    "        x_tr = x        \n",
    "        \n",
    "    elif tran_code == 2:\n",
    "        x_tr = x.diff()\n",
    "            \n",
    "    elif tran_code == 3:\n",
    "        x_tr = x.diff().diff()\n",
    "        \n",
    "    elif tran_code == 4:\n",
    "        x_tr = np.log(x)\n",
    "        \n",
    "    elif tran_code == 5:\n",
    "        x_tr = np.log(x).diff()*100\n",
    "    \n",
    "    elif tran_code == 6:\n",
    "        x_tr = np.log(x).diff().diff()*100\n",
    "    \n",
    "    else:\n",
    "        x_tr = (x.pct_change()-1)*100\n",
    "    \n",
    "    return x_tr    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d0ffbba8-52bc-434a-986d-e92e36dae467",
   "metadata": {},
   "outputs": [],
   "source": [
    "macro_tr=[]\n",
    "\n",
    "for col in macro.columns:\n",
    "    tr_code = tran_code[col].values\n",
    "    data_tr = data_transform(macro[col], tr_code)\n",
    "    macro_tr.append(data_tr)\n",
    "\n",
    "macro_tr = pd.DataFrame(macro_tr).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a9bfeadd-b7ae-429d-9da4-9fe8ec3a611f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>RPI</th>\n",
       "      <th>W875RX1</th>\n",
       "      <th>DPCERA3M086SBEA</th>\n",
       "      <th>CMRMTSPLx</th>\n",
       "      <th>RETAILx</th>\n",
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       "    <tr>\n",
       "      <th>sasdate</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-02</th>\n",
       "      <td>0.393140</td>\n",
       "      <td>0.361286</td>\n",
       "      <td>1.036756</td>\n",
       "      <td>0.735762</td>\n",
       "      <td>0.730985</td>\n",
       "      <td>1.939060</td>\n",
       "      <td>1.340746</td>\n",
       "      <td>0.862504</td>\n",
       "      <td>0.731050</td>\n",
       "      <td>0.523479</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-03</th>\n",
       "      <td>0.643011</td>\n",
       "      <td>0.735934</td>\n",
       "      <td>0.941009</td>\n",
       "      <td>-0.342293</td>\n",
       "      <td>0.832120</td>\n",
       "      <td>1.430562</td>\n",
       "      <td>0.603538</td>\n",
       "      <td>0.489413</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.939253</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.112407</td>\n",
       "      <td>0.026180</td>\n",
       "      <td>-0.002184</td>\n",
       "      <td>-0.814665</td>\n",
       "      <td>0.481883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.492915</td>\n",
       "      <td>0.413844</td>\n",
       "      <td>-1.479236</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-04</th>\n",
       "      <td>0.649412</td>\n",
       "      <td>0.704864</td>\n",
       "      <td>-0.363947</td>\n",
       "      <td>1.992210</td>\n",
       "      <td>0.061571</td>\n",
       "      <td>2.107542</td>\n",
       "      <td>1.433800</td>\n",
       "      <td>1.454467</td>\n",
       "      <td>1.565020</td>\n",
       "      <td>0.638320</td>\n",
       "      <td>...</td>\n",
       "      <td>0.134899</td>\n",
       "      <td>0.175237</td>\n",
       "      <td>-0.002163</td>\n",
       "      <td>1.220345</td>\n",
       "      <td>-0.488953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.213403</td>\n",
       "      <td>0.673430</td>\n",
       "      <td>2.492924</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-05</th>\n",
       "      <td>0.576311</td>\n",
       "      <td>0.661646</td>\n",
       "      <td>1.200535</td>\n",
       "      <td>0.679731</td>\n",
       "      <td>0.780340</td>\n",
       "      <td>1.495451</td>\n",
       "      <td>0.826987</td>\n",
       "      <td>0.958168</td>\n",
       "      <td>0.477012</td>\n",
       "      <td>2.014866</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.168653</td>\n",
       "      <td>-0.193673</td>\n",
       "      <td>-0.002143</td>\n",
       "      <td>-0.408972</td>\n",
       "      <td>-0.481929</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.282830</td>\n",
       "      <td>0.202040</td>\n",
       "      <td>-1.534223</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>0.087570</td>\n",
       "      <td>0.130699</td>\n",
       "      <td>1.316033</td>\n",
       "      <td>0.190398</td>\n",
       "      <td>2.764501</td>\n",
       "      <td>1.003181</td>\n",
       "      <td>0.343776</td>\n",
       "      <td>-0.014901</td>\n",
       "      <td>-0.334291</td>\n",
       "      <td>0.217564</td>\n",
       "      <td>...</td>\n",
       "      <td>1.425376</td>\n",
       "      <td>0.033243</td>\n",
       "      <td>0.170724</td>\n",
       "      <td>-0.004386</td>\n",
       "      <td>0.309575</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.071035</td>\n",
       "      <td>0.049043</td>\n",
       "      <td>-0.142513</td>\n",
       "      <td>20.2269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-02</th>\n",
       "      <td>0.103913</td>\n",
       "      <td>-0.004103</td>\n",
       "      <td>-0.028459</td>\n",
       "      <td>-0.417590</td>\n",
       "      <td>-0.660515</td>\n",
       "      <td>-0.005561</td>\n",
       "      <td>-0.196666</td>\n",
       "      <td>-0.129562</td>\n",
       "      <td>0.097723</td>\n",
       "      <td>0.212525</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.416042</td>\n",
       "      <td>-0.224790</td>\n",
       "      <td>-0.417543</td>\n",
       "      <td>-0.152983</td>\n",
       "      <td>-0.621376</td>\n",
       "      <td>2.1</td>\n",
       "      <td>-0.237727</td>\n",
       "      <td>-0.068985</td>\n",
       "      <td>-0.121427</td>\n",
       "      <td>20.2141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-03</th>\n",
       "      <td>0.259530</td>\n",
       "      <td>0.284085</td>\n",
       "      <td>-0.033085</td>\n",
       "      <td>-0.945104</td>\n",
       "      <td>-0.915446</td>\n",
       "      <td>0.146036</td>\n",
       "      <td>-0.076300</td>\n",
       "      <td>-0.030143</td>\n",
       "      <td>0.276299</td>\n",
       "      <td>-1.061013</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.645568</td>\n",
       "      <td>-0.081575</td>\n",
       "      <td>0.240091</td>\n",
       "      <td>0.027041</td>\n",
       "      <td>0.579697</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.505030</td>\n",
       "      <td>0.343295</td>\n",
       "      <td>-2.045412</td>\n",
       "      <td>22.0446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-04</th>\n",
       "      <td>-0.106924</td>\n",
       "      <td>-0.036149</td>\n",
       "      <td>0.235970</td>\n",
       "      <td>-0.227369</td>\n",
       "      <td>0.371920</td>\n",
       "      <td>0.507904</td>\n",
       "      <td>0.717250</td>\n",
       "      <td>0.973228</td>\n",
       "      <td>0.963931</td>\n",
       "      <td>4.140587</td>\n",
       "      <td>...</td>\n",
       "      <td>0.660174</td>\n",
       "      <td>0.107827</td>\n",
       "      <td>-0.139110</td>\n",
       "      <td>-0.150135</td>\n",
       "      <td>-0.233149</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-0.654981</td>\n",
       "      <td>-0.037264</td>\n",
       "      <td>0.128939</td>\n",
       "      <td>17.4930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-05</th>\n",
       "      <td>0.273402</td>\n",
       "      <td>0.297667</td>\n",
       "      <td>-0.025338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.344328</td>\n",
       "      <td>-0.159177</td>\n",
       "      <td>-0.050830</td>\n",
       "      <td>-0.088432</td>\n",
       "      <td>-0.113839</td>\n",
       "      <td>0.325745</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.701762</td>\n",
       "      <td>-0.135343</td>\n",
       "      <td>0.135161</td>\n",
       "      <td>0.057251</td>\n",
       "      <td>0.304983</td>\n",
       "      <td>-4.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.607056</td>\n",
       "      <td>17.3964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>773 rows × 127 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 RPI   W875RX1  DPCERA3M086SBEA  CMRMTSPLx   RETAILx  \\\n",
       "sasdate                                                                \n",
       "1959-01-01       NaN       NaN              NaN        NaN       NaN   \n",
       "1959-01-02  0.393140  0.361286         1.036756   0.735762  0.730985   \n",
       "1959-01-03  0.643011  0.735934         0.941009  -0.342293  0.832120   \n",
       "1959-01-04  0.649412  0.704864        -0.363947   1.992210  0.061571   \n",
       "1959-01-05  0.576311  0.661646         1.200535   0.679731  0.780340   \n",
       "...              ...       ...              ...        ...       ...   \n",
       "2023-01-01  0.087570  0.130699         1.316033   0.190398  2.764501   \n",
       "2023-01-02  0.103913 -0.004103        -0.028459  -0.417590 -0.660515   \n",
       "2023-01-03  0.259530  0.284085        -0.033085  -0.945104 -0.915446   \n",
       "2023-01-04 -0.106924 -0.036149         0.235970  -0.227369  0.371920   \n",
       "2023-01-05  0.273402  0.297667        -0.025338        NaN  0.344328   \n",
       "\n",
       "              INDPRO   IPFPNSS   IPFINAL   IPCONGD  IPDCONGD  ...  \\\n",
       "sasdate                                                       ...   \n",
       "1959-01-01       NaN       NaN       NaN       NaN       NaN  ...   \n",
       "1959-01-02  1.939060  1.340746  0.862504  0.731050  0.523479  ...   \n",
       "1959-01-03  1.430562  0.603538  0.489413  0.000000  1.939253  ...   \n",
       "1959-01-04  2.107542  1.433800  1.454467  1.565020  0.638320  ...   \n",
       "1959-01-05  1.495451  0.826987  0.958168  0.477012  2.014866  ...   \n",
       "...              ...       ...       ...       ...       ...  ...   \n",
       "2023-01-01  1.003181  0.343776 -0.014901 -0.334291  0.217564  ...   \n",
       "2023-01-02 -0.005561 -0.196666 -0.129562  0.097723  0.212525  ...   \n",
       "2023-01-03  0.146036 -0.076300 -0.030143  0.276299 -1.061013  ...   \n",
       "2023-01-04  0.507904  0.717250  0.973228  0.963931  4.140587  ...   \n",
       "2023-01-05 -0.159177 -0.050830 -0.088432 -0.113839  0.325745  ...   \n",
       "\n",
       "            DNDGRG3M086SBEA  DSERRG3M086SBEA  CES0600000008  CES2000000008  \\\n",
       "sasdate                                                                      \n",
       "1959-01-01              NaN              NaN            NaN            NaN   \n",
       "1959-01-02              NaN              NaN            NaN            NaN   \n",
       "1959-01-03        -0.112407         0.026180      -0.002184      -0.814665   \n",
       "1959-01-04         0.134899         0.175237      -0.002163       1.220345   \n",
       "1959-01-05        -0.168653        -0.193673      -0.002143      -0.408972   \n",
       "...                     ...              ...            ...            ...   \n",
       "2023-01-01         1.425376         0.033243       0.170724      -0.004386   \n",
       "2023-01-02        -0.416042        -0.224790      -0.417543      -0.152983   \n",
       "2023-01-03        -0.645568        -0.081575       0.240091       0.027041   \n",
       "2023-01-04         0.660174         0.107827      -0.139110      -0.150135   \n",
       "2023-01-05        -0.701762        -0.135343       0.135161       0.057251   \n",
       "\n",
       "            CES3000000008  UMCSENTx  DTCOLNVHFNM  DTCTHFNM    INVEST  VIXCLSx  \n",
       "sasdate                                                                        \n",
       "1959-01-01            NaN       NaN          NaN       NaN       NaN      NaN  \n",
       "1959-01-02            NaN       NaN          NaN       NaN       NaN      NaN  \n",
       "1959-01-03       0.481883       NaN     0.492915  0.413844 -1.479236      NaN  \n",
       "1959-01-04      -0.488953       NaN     1.213403  0.673430  2.492924      NaN  \n",
       "1959-01-05      -0.481929       NaN     0.282830  0.202040 -1.534223      NaN  \n",
       "...                   ...       ...          ...       ...       ...      ...  \n",
       "2023-01-01       0.309575       5.2     0.071035  0.049043 -0.142513  20.2269  \n",
       "2023-01-02      -0.621376       2.1    -0.237727 -0.068985 -0.121427  20.2141  \n",
       "2023-01-03       0.579697      -5.0     0.505030  0.343295 -2.045412  22.0446  \n",
       "2023-01-04      -0.233149       1.5    -0.654981 -0.037264  0.128939  17.4930  \n",
       "2023-01-05       0.304983      -4.3          NaN       NaN  1.607056  17.3964  \n",
       "\n",
       "[773 rows x 127 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e12b3c5c-c729-4638-913a-b6ac1a637045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sasdate\n",
       "1959-01-01     NaN\n",
       "1959-01-02   -0.05\n",
       "1959-01-03    0.37\n",
       "1959-01-04    0.16\n",
       "1959-01-05   -0.06\n",
       "              ... \n",
       "2023-01-01    0.23\n",
       "2023-01-02    0.24\n",
       "2023-01-03    0.08\n",
       "2023-01-04    0.18\n",
       "2023-01-05    0.23\n",
       "Name: FEDFUNDS, Length: 773, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_column = 'FEDFUNDS'\n",
    "EFFR = macro_tr[target_column]\n",
    "macro_tr = macro_tr.drop(columns=[target_column])\n",
    "macro_tr[target_column] = EFFR\n",
    "EFFR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "cdb39b5f-f1ac-4d98-a507-c17a2cf354a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows, Columns:\n",
      "(773, 127)\n",
      "\n",
      "\n",
      "Columns:\n",
      "Index(['RPI', 'W875RX1', 'DPCERA3M086SBEA', 'CMRMTSPLx', 'RETAILx', 'INDPRO',\n",
      "       'IPFPNSS', 'IPFINAL', 'IPCONGD', 'IPDCONGD',\n",
      "       ...\n",
      "       'DSERRG3M086SBEA', 'CES0600000008', 'CES2000000008', 'CES3000000008',\n",
      "       'UMCSENTx', 'DTCOLNVHFNM', 'DTCTHFNM', 'INVEST', 'VIXCLSx', 'FEDFUNDS'],\n",
      "      dtype='object', length=127)\n",
      "\n",
      "\n",
      "Info:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 773 entries, 1959-01-01 to 2023-01-05\n",
      "Data columns (total 127 columns):\n",
      " #    Column           Non-Null Count  Dtype  \n",
      "---   ------           --------------  -----  \n",
      " 0    RPI              772 non-null    float64\n",
      " 1    W875RX1          772 non-null    float64\n",
      " 2    DPCERA3M086SBEA  772 non-null    float64\n",
      " 3    CMRMTSPLx        771 non-null    float64\n",
      " 4    RETAILx          772 non-null    float64\n",
      " 5    INDPRO           772 non-null    float64\n",
      " 6    IPFPNSS          772 non-null    float64\n",
      " 7    IPFINAL          772 non-null    float64\n",
      " 8    IPCONGD          772 non-null    float64\n",
      " 9    IPDCONGD         772 non-null    float64\n",
      " 10   IPNCONGD         772 non-null    float64\n",
      " 11   IPBUSEQ          772 non-null    float64\n",
      " 12   IPMAT            772 non-null    float64\n",
      " 13   IPDMAT           772 non-null    float64\n",
      " 14   IPNMAT           772 non-null    float64\n",
      " 15   IPMANSICS        772 non-null    float64\n",
      " 16   IPB51222S        772 non-null    float64\n",
      " 17   IPFUELS          772 non-null    float64\n",
      " 18   CUMFNS           772 non-null    float64\n",
      " 19   HWI              771 non-null    float64\n",
      " 20   HWIURATIO        771 non-null    float64\n",
      " 21   CLF16OV          772 non-null    float64\n",
      " 22   CE16OV           772 non-null    float64\n",
      " 23   UNRATE           772 non-null    float64\n",
      " 24   UEMPMEAN         772 non-null    float64\n",
      " 25   UEMPLT5          772 non-null    float64\n",
      " 26   UEMP5TO14        772 non-null    float64\n",
      " 27   UEMP15OV         772 non-null    float64\n",
      " 28   UEMP15T26        772 non-null    float64\n",
      " 29   UEMP27OV         772 non-null    float64\n",
      " 30   CLAIMSx          772 non-null    float64\n",
      " 31   PAYEMS           772 non-null    float64\n",
      " 32   USGOOD           772 non-null    float64\n",
      " 33   CES1021000001    772 non-null    float64\n",
      " 34   USCONS           772 non-null    float64\n",
      " 35   MANEMP           772 non-null    float64\n",
      " 36   DMANEMP          772 non-null    float64\n",
      " 37   NDMANEMP         772 non-null    float64\n",
      " 38   SRVPRD           772 non-null    float64\n",
      " 39   USTPU            772 non-null    float64\n",
      " 40   USWTRADE         772 non-null    float64\n",
      " 41   USTRADE          772 non-null    float64\n",
      " 42   USFIRE           772 non-null    float64\n",
      " 43   USGOVT           772 non-null    float64\n",
      " 44   CES0600000007    773 non-null    float64\n",
      " 45   AWOTMAN          772 non-null    float64\n",
      " 46   AWHMAN           773 non-null    float64\n",
      " 47   HOUST            773 non-null    float64\n",
      " 48   HOUSTNE          773 non-null    float64\n",
      " 49   HOUSTMW          773 non-null    float64\n",
      " 50   HOUSTS           773 non-null    float64\n",
      " 51   HOUSTW           773 non-null    float64\n",
      " 52   PERMIT           761 non-null    float64\n",
      " 53   PERMITNE         761 non-null    float64\n",
      " 54   PERMITMW         761 non-null    float64\n",
      " 55   PERMITS          761 non-null    float64\n",
      " 56   PERMITW          761 non-null    float64\n",
      " 57   ACOGNO           374 non-null    float64\n",
      " 58   AMDMNOx          772 non-null    float64\n",
      " 59   ANDENOx          663 non-null    float64\n",
      " 60   AMDMUOx          772 non-null    float64\n",
      " 61   BUSINVx          771 non-null    float64\n",
      " 62   ISRATIOx         771 non-null    float64\n",
      " 63   M1SL             771 non-null    float64\n",
      " 64   M2SL             771 non-null    float64\n",
      " 65   M2REAL           772 non-null    float64\n",
      " 66   BOGMBASE         771 non-null    float64\n",
      " 67   TOTRESNS         771 non-null    float64\n",
      " 68   NONBORRES        772 non-null    float64\n",
      " 69   BUSLOANS         771 non-null    float64\n",
      " 70   REALLN           771 non-null    float64\n",
      " 71   NONREVSL         770 non-null    float64\n",
      " 72   CONSPI           771 non-null    float64\n",
      " 73   S&P 500          772 non-null    float64\n",
      " 74   S&P: indust      772 non-null    float64\n",
      " 75   S&P div yield    770 non-null    float64\n",
      " 76   S&P PE ratio     771 non-null    float64\n",
      " 77   CP3Mx            770 non-null    float64\n",
      " 78   TB3MS            772 non-null    float64\n",
      " 79   TB6MS            772 non-null    float64\n",
      " 80   GS1              772 non-null    float64\n",
      " 81   GS5              772 non-null    float64\n",
      " 82   GS10             772 non-null    float64\n",
      " 83   AAA              772 non-null    float64\n",
      " 84   BAA              772 non-null    float64\n",
      " 85   COMPAPFFx        772 non-null    float64\n",
      " 86   TB3SMFFM         773 non-null    float64\n",
      " 87   TB6SMFFM         773 non-null    float64\n",
      " 88   T1YFFM           773 non-null    float64\n",
      " 89   T5YFFM           773 non-null    float64\n",
      " 90   T10YFFM          773 non-null    float64\n",
      " 91   AAAFFM           773 non-null    float64\n",
      " 92   BAAFFM           773 non-null    float64\n",
      " 93   TWEXAFEGSMTHx    604 non-null    float64\n",
      " 94   EXSZUSx          772 non-null    float64\n",
      " 95   EXJPUSx          772 non-null    float64\n",
      " 96   EXUSUKx          772 non-null    float64\n",
      " 97   EXCAUSx          772 non-null    float64\n",
      " 98   WPSFD49207       771 non-null    float64\n",
      " 99   WPSFD49502       771 non-null    float64\n",
      " 100  WPSID61          771 non-null    float64\n",
      " 101  WPSID62          771 non-null    float64\n",
      " 102  OILPRICEx        771 non-null    float64\n",
      " 103  PPICMM           771 non-null    float64\n",
      " 104  CPIAUCSL         771 non-null    float64\n",
      " 105  CPIAPPSL         771 non-null    float64\n",
      " 106  CPITRNSL         771 non-null    float64\n",
      " 107  CPIMEDSL         771 non-null    float64\n",
      " 108  CUSR0000SAC      771 non-null    float64\n",
      " 109  CUSR0000SAD      771 non-null    float64\n",
      " 110  CUSR0000SAS      771 non-null    float64\n",
      " 111  CPIULFSL         771 non-null    float64\n",
      " 112  CUSR0000SA0L2    771 non-null    float64\n",
      " 113  CUSR0000SA0L5    771 non-null    float64\n",
      " 114  PCEPI            771 non-null    float64\n",
      " 115  DDURRG3M086SBEA  771 non-null    float64\n",
      " 116  DNDGRG3M086SBEA  771 non-null    float64\n",
      " 117  DSERRG3M086SBEA  771 non-null    float64\n",
      " 118  CES0600000008    771 non-null    float64\n",
      " 119  CES2000000008    771 non-null    float64\n",
      " 120  CES3000000008    771 non-null    float64\n",
      " 121  UMCSENTx         544 non-null    float64\n",
      " 122  DTCOLNVHFNM      770 non-null    float64\n",
      " 123  DTCTHFNM         770 non-null    float64\n",
      " 124  INVEST           771 non-null    float64\n",
      " 125  VIXCLSx          731 non-null    float64\n",
      " 126  FEDFUNDS         772 non-null    float64\n",
      "dtypes: float64(127)\n",
      "memory usage: 789.2 KB\n",
      "None\n",
      "\n",
      "\n",
      "Non-NA:\n",
      "RPI                772\n",
      "W875RX1            772\n",
      "DPCERA3M086SBEA    772\n",
      "CMRMTSPLx          771\n",
      "RETAILx            772\n",
      "INDPRO             772\n",
      "IPFPNSS            772\n",
      "IPFINAL            772\n",
      "IPCONGD            772\n",
      "IPDCONGD           772\n",
      "IPNCONGD           772\n",
      "IPBUSEQ            772\n",
      "IPMAT              772\n",
      "IPDMAT             772\n",
      "IPNMAT             772\n",
      "IPMANSICS          772\n",
      "IPB51222S          772\n",
      "IPFUELS            772\n",
      "CUMFNS             772\n",
      "HWI                771\n",
      "HWIURATIO          771\n",
      "CLF16OV            772\n",
      "CE16OV             772\n",
      "UNRATE             772\n",
      "UEMPMEAN           772\n",
      "UEMPLT5            772\n",
      "UEMP5TO14          772\n",
      "UEMP15OV           772\n",
      "UEMP15T26          772\n",
      "UEMP27OV           772\n",
      "CLAIMSx            772\n",
      "PAYEMS             772\n",
      "USGOOD             772\n",
      "CES1021000001      772\n",
      "USCONS             772\n",
      "MANEMP             772\n",
      "DMANEMP            772\n",
      "NDMANEMP           772\n",
      "SRVPRD             772\n",
      "USTPU              772\n",
      "USWTRADE           772\n",
      "USTRADE            772\n",
      "USFIRE             772\n",
      "USGOVT             772\n",
      "CES0600000007      773\n",
      "AWOTMAN            772\n",
      "AWHMAN             773\n",
      "HOUST              773\n",
      "HOUSTNE            773\n",
      "HOUSTMW            773\n",
      "HOUSTS             773\n",
      "HOUSTW             773\n",
      "PERMIT             761\n",
      "PERMITNE           761\n",
      "PERMITMW           761\n",
      "PERMITS            761\n",
      "PERMITW            761\n",
      "ACOGNO             374\n",
      "AMDMNOx            772\n",
      "ANDENOx            663\n",
      "AMDMUOx            772\n",
      "BUSINVx            771\n",
      "ISRATIOx           771\n",
      "M1SL               771\n",
      "M2SL               771\n",
      "M2REAL             772\n",
      "BOGMBASE           771\n",
      "TOTRESNS           771\n",
      "NONBORRES          772\n",
      "BUSLOANS           771\n",
      "REALLN             771\n",
      "NONREVSL           770\n",
      "CONSPI             771\n",
      "S&P 500            772\n",
      "S&P: indust        772\n",
      "S&P div yield      770\n",
      "S&P PE ratio       771\n",
      "CP3Mx              770\n",
      "TB3MS              772\n",
      "TB6MS              772\n",
      "GS1                772\n",
      "GS5                772\n",
      "GS10               772\n",
      "AAA                772\n",
      "BAA                772\n",
      "COMPAPFFx          772\n",
      "TB3SMFFM           773\n",
      "TB6SMFFM           773\n",
      "T1YFFM             773\n",
      "T5YFFM             773\n",
      "T10YFFM            773\n",
      "AAAFFM             773\n",
      "BAAFFM             773\n",
      "TWEXAFEGSMTHx      604\n",
      "EXSZUSx            772\n",
      "EXJPUSx            772\n",
      "EXUSUKx            772\n",
      "EXCAUSx            772\n",
      "WPSFD49207         771\n",
      "WPSFD49502         771\n",
      "WPSID61            771\n",
      "WPSID62            771\n",
      "OILPRICEx          771\n",
      "PPICMM             771\n",
      "CPIAUCSL           771\n",
      "CPIAPPSL           771\n",
      "CPITRNSL           771\n",
      "CPIMEDSL           771\n",
      "CUSR0000SAC        771\n",
      "CUSR0000SAD        771\n",
      "CUSR0000SAS        771\n",
      "CPIULFSL           771\n",
      "CUSR0000SA0L2      771\n",
      "CUSR0000SA0L5      771\n",
      "PCEPI              771\n",
      "DDURRG3M086SBEA    771\n",
      "DNDGRG3M086SBEA    771\n",
      "DSERRG3M086SBEA    771\n",
      "CES0600000008      771\n",
      "CES2000000008      771\n",
      "CES3000000008      771\n",
      "UMCSENTx           544\n",
      "DTCOLNVHFNM        770\n",
      "DTCTHFNM           770\n",
      "INVEST             771\n",
      "VIXCLSx            731\n",
      "FEDFUNDS           772\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "Head\n",
      "                 RPI   W875RX1  DPCERA3M086SBEA  CMRMTSPLx   RETAILx  \\\n",
      "sasdate                                                                \n",
      "1959-01-01       NaN       NaN              NaN        NaN       NaN   \n",
      "1959-01-02  0.393140  0.361286         1.036756   0.735762  0.730985   \n",
      "1959-01-03  0.643011  0.735934         0.941009  -0.342293  0.832120   \n",
      "1959-01-04  0.649412  0.704864        -0.363947   1.992210  0.061571   \n",
      "1959-01-05  0.576311  0.661646         1.200535   0.679731  0.780340   \n",
      "\n",
      "              INDPRO   IPFPNSS   IPFINAL   IPCONGD  IPDCONGD  ...  \\\n",
      "sasdate                                                       ...   \n",
      "1959-01-01       NaN       NaN       NaN       NaN       NaN  ...   \n",
      "1959-01-02  1.939060  1.340746  0.862504  0.731050  0.523479  ...   \n",
      "1959-01-03  1.430562  0.603538  0.489413  0.000000  1.939253  ...   \n",
      "1959-01-04  2.107542  1.433800  1.454467  1.565020  0.638320  ...   \n",
      "1959-01-05  1.495451  0.826987  0.958168  0.477012  2.014866  ...   \n",
      "\n",
      "            DSERRG3M086SBEA  CES0600000008  CES2000000008  CES3000000008  \\\n",
      "sasdate                                                                    \n",
      "1959-01-01              NaN            NaN            NaN            NaN   \n",
      "1959-01-02              NaN            NaN            NaN            NaN   \n",
      "1959-01-03         0.026180      -0.002184      -0.814665       0.481883   \n",
      "1959-01-04         0.175237      -0.002163       1.220345      -0.488953   \n",
      "1959-01-05        -0.193673      -0.002143      -0.408972      -0.481929   \n",
      "\n",
      "            UMCSENTx  DTCOLNVHFNM  DTCTHFNM    INVEST  VIXCLSx  FEDFUNDS  \n",
      "sasdate                                                                   \n",
      "1959-01-01       NaN          NaN       NaN       NaN      NaN       NaN  \n",
      "1959-01-02       NaN          NaN       NaN       NaN      NaN     -0.05  \n",
      "1959-01-03       NaN     0.492915  0.413844 -1.479236      NaN      0.37  \n",
      "1959-01-04       NaN     1.213403  0.673430  2.492924      NaN      0.16  \n",
      "1959-01-05       NaN     0.282830  0.202040 -1.534223      NaN     -0.06  \n",
      "\n",
      "[5 rows x 127 columns]\n",
      "\n",
      "\n",
      "Tail\n",
      "                 RPI   W875RX1  DPCERA3M086SBEA  CMRMTSPLx   RETAILx  \\\n",
      "sasdate                                                                \n",
      "2023-01-01  0.087570  0.130699         1.316033   0.190398  2.764501   \n",
      "2023-01-02  0.103913 -0.004103        -0.028459  -0.417590 -0.660515   \n",
      "2023-01-03  0.259530  0.284085        -0.033085  -0.945104 -0.915446   \n",
      "2023-01-04 -0.106924 -0.036149         0.235970  -0.227369  0.371920   \n",
      "2023-01-05  0.273402  0.297667        -0.025338        NaN  0.344328   \n",
      "\n",
      "              INDPRO   IPFPNSS   IPFINAL   IPCONGD  IPDCONGD  ...  \\\n",
      "sasdate                                                       ...   \n",
      "2023-01-01  1.003181  0.343776 -0.014901 -0.334291  0.217564  ...   \n",
      "2023-01-02 -0.005561 -0.196666 -0.129562  0.097723  0.212525  ...   \n",
      "2023-01-03  0.146036 -0.076300 -0.030143  0.276299 -1.061013  ...   \n",
      "2023-01-04  0.507904  0.717250  0.973228  0.963931  4.140587  ...   \n",
      "2023-01-05 -0.159177 -0.050830 -0.088432 -0.113839  0.325745  ...   \n",
      "\n",
      "            DSERRG3M086SBEA  CES0600000008  CES2000000008  CES3000000008  \\\n",
      "sasdate                                                                    \n",
      "2023-01-01         0.033243       0.170724      -0.004386       0.309575   \n",
      "2023-01-02        -0.224790      -0.417543      -0.152983      -0.621376   \n",
      "2023-01-03        -0.081575       0.240091       0.027041       0.579697   \n",
      "2023-01-04         0.107827      -0.139110      -0.150135      -0.233149   \n",
      "2023-01-05        -0.135343       0.135161       0.057251       0.304983   \n",
      "\n",
      "            UMCSENTx  DTCOLNVHFNM  DTCTHFNM    INVEST  VIXCLSx  FEDFUNDS  \n",
      "sasdate                                                                   \n",
      "2023-01-01       5.2     0.071035  0.049043 -0.142513  20.2269      0.23  \n",
      "2023-01-02       2.1    -0.237727 -0.068985 -0.121427  20.2141      0.24  \n",
      "2023-01-03      -5.0     0.505030  0.343295 -2.045412  22.0446      0.08  \n",
      "2023-01-04       1.5    -0.654981 -0.037264  0.128939  17.4930      0.18  \n",
      "2023-01-05      -4.3          NaN       NaN  1.607056  17.3964      0.23  \n",
      "\n",
      "[5 rows x 127 columns]\n",
      "\n",
      "\n",
      "Summary statistics:\n",
      "              RPI     W875RX1  DPCERA3M086SBEA   CMRMTSPLx     RETAILx  \\\n",
      "count  772.000000  772.000000       772.000000  771.000000  772.000000   \n",
      "mean     0.258134    0.240684         0.261678    0.216966    0.469991   \n",
      "std      1.183156    0.613966         0.835187    1.267926    1.560335   \n",
      "min    -14.565065   -6.056840       -12.799379  -12.889057  -15.809242   \n",
      "25%      0.057620    0.019656        -0.009627   -0.391493   -0.100021   \n",
      "50%      0.273854    0.284552         0.256440    0.220600    0.485266   \n",
      "75%      0.479736    0.494470         0.569047    0.843807    1.074441   \n",
      "max     18.234919    4.012112         8.208242    7.732231   17.405309   \n",
      "\n",
      "           INDPRO     IPFPNSS     IPFINAL     IPCONGD    IPDCONGD  ...  \\\n",
      "count  772.000000  772.000000  772.000000  772.000000  772.000000  ...   \n",
      "mean     0.200170    0.190365    0.197207    0.153057    0.225858  ...   \n",
      "std      1.006339    0.970108    1.062247    1.065949    3.347475  ...   \n",
      "min    -14.365634  -14.656939  -15.702967  -12.407525  -49.668255  ...   \n",
      "25%     -0.172147   -0.226611   -0.245685   -0.348582   -0.820171  ...   \n",
      "50%      0.246941    0.236541    0.221127    0.169886    0.292143  ...   \n",
      "75%      0.645452    0.646329    0.694881    0.664855    1.332917  ...   \n",
      "max      6.298618    6.870275    8.287202    8.532604   35.663749  ...   \n",
      "\n",
      "       DSERRG3M086SBEA  CES0600000008  CES2000000008  CES3000000008  \\\n",
      "count       771.000000     771.000000     771.000000     771.000000   \n",
      "mean          0.000138       0.000012       0.000044       0.000161   \n",
      "std           0.161782       0.396574       0.894516       0.475667   \n",
      "min          -0.944192      -2.950920      -6.807429      -2.686752   \n",
      "25%          -0.084273      -0.221739      -0.339970      -0.243844   \n",
      "50%          -0.007507      -0.001189      -0.002556      -0.001110   \n",
      "75%           0.079727       0.203186       0.342729       0.218216   \n",
      "max           1.597500       1.930253       5.305046       2.482810   \n",
      "\n",
      "         UMCSENTx  DTCOLNVHFNM    DTCTHFNM      INVEST     VIXCLSx    FEDFUNDS  \n",
      "count  544.000000   770.000000  770.000000  771.000000  731.000000  772.000000  \n",
      "mean    -0.045037     0.000407    0.000088    0.000307   19.400722    0.003342  \n",
      "std      4.022466     2.366239    1.965352    1.085644    7.118632    0.501225  \n",
      "min    -17.300000   -17.590365  -25.956229   -8.470116    9.484400   -6.630000  \n",
      "25%     -2.400000    -0.682911   -0.397565   -0.588019   14.334650   -0.080000  \n",
      "50%     -0.100000     0.056028    0.055736    0.042164   17.669500    0.010000  \n",
      "75%      2.500000     0.677473    0.488217    0.599093   22.588350    0.120000  \n",
      "max     17.300000    18.730787   25.805015    4.994849   62.964800    3.060000  \n",
      "\n",
      "[8 rows x 127 columns]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Show rows and columns\n",
    "print(\"Rows, Columns:\");print(macro_tr.shape);print(\"\\n\")\n",
    "\n",
    "# Describe DataFrame columns\n",
    "print(\"Columns:\");print(macro_tr.columns);print(\"\\n\")\n",
    "\n",
    "# Show info on DataFrame\n",
    "print(\"Info:\");print(macro_tr.info(max_cols=1000)); print(\"\\n\")\n",
    "\n",
    "# Count Non-NA values\n",
    "print(\"Non-NA:\");print(macro_tr.count()); print(\"\\n\")\n",
    "\n",
    "# Show head\n",
    "print(\"Head\");print(macro_tr.head()); print(\"\\n\")\n",
    "\n",
    "# Show tail\n",
    "print(\"Tail\");print(macro_tr.tail());print(\"\\n\")\n",
    "\n",
    "# Show summary statistics\n",
    "print(\"Summary statistics:\");print(macro_tr.describe());print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fb7325ca-cdbb-475e-9cbd-74f0a8159ca7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ACOGNO', 'ANDENOx', 'TWEXAFEGSMTHx', 'UMCSENTx', 'VIXCLSx'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "threshold = 0.05\n",
    "missing_ratio_c = macro_tr.isnull().mean()\n",
    "columns_to_drop = missing_ratio_c[missing_ratio_c > threshold].index\n",
    "macro_tr_cleaned_c = macro_tr.drop(columns=columns_to_drop)\n",
    "print(columns_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e94add2f-49a2-4538-b47e-8870f1d4cf85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['1959-01-01', '1959-01-02'], dtype='datetime64[ns]', name='sasdate', freq=None)\n"
     ]
    }
   ],
   "source": [
    "threshold = 0.3\n",
    "missing_ratio_r = macro_tr.isnull().mean(axis=1)\n",
    "rows_to_drop = missing_ratio_r[missing_ratio_r > threshold].index\n",
    "macro_tr_cleaned_r = macro_tr_cleaned_c.drop(rows_to_drop)\n",
    "print(rows_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "571240b3-b011-4feb-ad5c-b1199283b2ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "macro_final = macro_tr_cleaned_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "44847bf3-a9c1-4cf4-bfa9-68e67766fce3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define Data for Machine Learning\n",
    "\n",
    "X_start = 0 \n",
    "X_lag = 1\n",
    "\n",
    "# Features and Target\n",
    "X  = macro_final.iloc[X_start:,:-1].shift(X_lag) \n",
    "y  = macro_final.iloc[X_start:,121]\n",
    "\n",
    "X = X.dropna()\n",
    "\n",
    "y = y.loc[X.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3488eb4d-3ee7-45fe-bf97-c24647a9ef97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPI</th>\n",
       "      <th>W875RX1</th>\n",
       "      <th>DPCERA3M086SBEA</th>\n",
       "      <th>CMRMTSPLx</th>\n",
       "      <th>RETAILx</th>\n",
       "      <th>INDPRO</th>\n",
       "      <th>IPFPNSS</th>\n",
       "      <th>IPFINAL</th>\n",
       "      <th>IPCONGD</th>\n",
       "      <th>IPDCONGD</th>\n",
       "      <th>...</th>\n",
       "      <th>PCEPI</th>\n",
       "      <th>DDURRG3M086SBEA</th>\n",
       "      <th>DNDGRG3M086SBEA</th>\n",
       "      <th>DSERRG3M086SBEA</th>\n",
       "      <th>CES0600000008</th>\n",
       "      <th>CES2000000008</th>\n",
       "      <th>CES3000000008</th>\n",
       "      <th>DTCOLNVHFNM</th>\n",
       "      <th>DTCTHFNM</th>\n",
       "      <th>INVEST</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sasdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-02</th>\n",
       "      <td>0.319409</td>\n",
       "      <td>0.463825</td>\n",
       "      <td>0.278847</td>\n",
       "      <td>1.695646</td>\n",
       "      <td>2.660551</td>\n",
       "      <td>2.591713</td>\n",
       "      <td>2.409495</td>\n",
       "      <td>2.902087</td>\n",
       "      <td>3.123538</td>\n",
       "      <td>10.383083</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.171620</td>\n",
       "      <td>-0.259079</td>\n",
       "      <td>-0.178584</td>\n",
       "      <td>-0.128845</td>\n",
       "      <td>-0.468036</td>\n",
       "      <td>0.779728</td>\n",
       "      <td>-0.970531</td>\n",
       "      <td>0.429236</td>\n",
       "      <td>-1.173886</td>\n",
       "      <td>-1.333027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-03</th>\n",
       "      <td>0.114254</td>\n",
       "      <td>0.091674</td>\n",
       "      <td>0.433454</td>\n",
       "      <td>1.437148</td>\n",
       "      <td>0.369645</td>\n",
       "      <td>-0.893885</td>\n",
       "      <td>-0.568540</td>\n",
       "      <td>-0.343503</td>\n",
       "      <td>-1.145653</td>\n",
       "      <td>-1.385485</td>\n",
       "      <td>...</td>\n",
       "      <td>0.159303</td>\n",
       "      <td>0.376462</td>\n",
       "      <td>0.167438</td>\n",
       "      <td>0.085463</td>\n",
       "      <td>-0.455545</td>\n",
       "      <td>0.385334</td>\n",
       "      <td>-0.475018</td>\n",
       "      <td>0.826845</td>\n",
       "      <td>0.538766</td>\n",
       "      <td>-1.894229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-04</th>\n",
       "      <td>0.190876</td>\n",
       "      <td>0.091590</td>\n",
       "      <td>1.404092</td>\n",
       "      <td>-2.802612</td>\n",
       "      <td>-0.110199</td>\n",
       "      <td>-0.901948</td>\n",
       "      <td>-0.342682</td>\n",
       "      <td>-0.115042</td>\n",
       "      <td>0.115277</td>\n",
       "      <td>-1.996461</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.049056</td>\n",
       "      <td>-0.455511</td>\n",
       "      <td>0.178561</td>\n",
       "      <td>-0.111313</td>\n",
       "      <td>-0.002011</td>\n",
       "      <td>3.013134</td>\n",
       "      <td>-0.468385</td>\n",
       "      <td>0.347209</td>\n",
       "      <td>0.095090</td>\n",
       "      <td>0.348999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-05</th>\n",
       "      <td>0.341874</td>\n",
       "      <td>0.361379</td>\n",
       "      <td>1.535528</td>\n",
       "      <td>0.982647</td>\n",
       "      <td>2.590339</td>\n",
       "      <td>-0.795880</td>\n",
       "      <td>0.228983</td>\n",
       "      <td>0.115042</td>\n",
       "      <td>0.687998</td>\n",
       "      <td>-0.118913</td>\n",
       "      <td>...</td>\n",
       "      <td>0.311430</td>\n",
       "      <td>0.411911</td>\n",
       "      <td>0.443860</td>\n",
       "      <td>0.145034</td>\n",
       "      <td>-0.894856</td>\n",
       "      <td>-6.807429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.867818</td>\n",
       "      <td>0.545173</td>\n",
       "      <td>2.413598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-06</th>\n",
       "      <td>0.240054</td>\n",
       "      <td>0.244332</td>\n",
       "      <td>-2.034337</td>\n",
       "      <td>-3.153052</td>\n",
       "      <td>-1.504683</td>\n",
       "      <td>-0.114277</td>\n",
       "      <td>0.568790</td>\n",
       "      <td>0.686244</td>\n",
       "      <td>0.569884</td>\n",
       "      <td>0.945486</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.262920</td>\n",
       "      <td>-0.129782</td>\n",
       "      <td>-0.672338</td>\n",
       "      <td>0.059133</td>\n",
       "      <td>0.894856</td>\n",
       "      <td>4.161967</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.482728</td>\n",
       "      <td>-0.206712</td>\n",
       "      <td>0.379810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-12</th>\n",
       "      <td>0.000898</td>\n",
       "      <td>-0.075962</td>\n",
       "      <td>-0.414455</td>\n",
       "      <td>-0.947270</td>\n",
       "      <td>-1.320097</td>\n",
       "      <td>-0.330005</td>\n",
       "      <td>-0.332552</td>\n",
       "      <td>-0.400833</td>\n",
       "      <td>-0.179349</td>\n",
       "      <td>-2.240469</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.248867</td>\n",
       "      <td>-0.174864</td>\n",
       "      <td>-0.884680</td>\n",
       "      <td>-0.056635</td>\n",
       "      <td>0.102595</td>\n",
       "      <td>-0.062401</td>\n",
       "      <td>0.389154</td>\n",
       "      <td>0.162379</td>\n",
       "      <td>-0.007056</td>\n",
       "      <td>0.959749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>-0.013781</td>\n",
       "      <td>-0.019171</td>\n",
       "      <td>-0.193846</td>\n",
       "      <td>1.490076</td>\n",
       "      <td>-0.719328</td>\n",
       "      <td>-1.550613</td>\n",
       "      <td>-1.202485</td>\n",
       "      <td>-0.846966</td>\n",
       "      <td>-0.623823</td>\n",
       "      <td>-1.031771</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030879</td>\n",
       "      <td>0.344041</td>\n",
       "      <td>-0.613838</td>\n",
       "      <td>0.175427</td>\n",
       "      <td>-0.107940</td>\n",
       "      <td>0.300155</td>\n",
       "      <td>-0.590025</td>\n",
       "      <td>-0.283245</td>\n",
       "      <td>-0.123254</td>\n",
       "      <td>0.880929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-02</th>\n",
       "      <td>0.087570</td>\n",
       "      <td>0.130699</td>\n",
       "      <td>1.316033</td>\n",
       "      <td>0.190398</td>\n",
       "      <td>2.764501</td>\n",
       "      <td>1.003181</td>\n",
       "      <td>0.343776</td>\n",
       "      <td>-0.014901</td>\n",
       "      <td>-0.334291</td>\n",
       "      <td>0.217564</td>\n",
       "      <td>...</td>\n",
       "      <td>0.382455</td>\n",
       "      <td>0.474433</td>\n",
       "      <td>1.425376</td>\n",
       "      <td>0.033243</td>\n",
       "      <td>0.170724</td>\n",
       "      <td>-0.004386</td>\n",
       "      <td>0.309575</td>\n",
       "      <td>0.071035</td>\n",
       "      <td>0.049043</td>\n",
       "      <td>-0.142513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-03</th>\n",
       "      <td>0.103913</td>\n",
       "      <td>-0.004103</td>\n",
       "      <td>-0.028459</td>\n",
       "      <td>-0.417590</td>\n",
       "      <td>-0.660515</td>\n",
       "      <td>-0.005561</td>\n",
       "      <td>-0.196666</td>\n",
       "      <td>-0.129562</td>\n",
       "      <td>0.097723</td>\n",
       "      <td>0.212525</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.293685</td>\n",
       "      <td>-0.436099</td>\n",
       "      <td>-0.416042</td>\n",
       "      <td>-0.224790</td>\n",
       "      <td>-0.417543</td>\n",
       "      <td>-0.152983</td>\n",
       "      <td>-0.621376</td>\n",
       "      <td>-0.237727</td>\n",
       "      <td>-0.068985</td>\n",
       "      <td>-0.121427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-04</th>\n",
       "      <td>0.259530</td>\n",
       "      <td>0.284085</td>\n",
       "      <td>-0.033085</td>\n",
       "      <td>-0.945104</td>\n",
       "      <td>-0.915446</td>\n",
       "      <td>0.146036</td>\n",
       "      <td>-0.076300</td>\n",
       "      <td>-0.030143</td>\n",
       "      <td>0.276299</td>\n",
       "      <td>-1.061013</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.175550</td>\n",
       "      <td>0.100107</td>\n",
       "      <td>-0.645568</td>\n",
       "      <td>-0.081575</td>\n",
       "      <td>0.240091</td>\n",
       "      <td>0.027041</td>\n",
       "      <td>0.579697</td>\n",
       "      <td>0.505030</td>\n",
       "      <td>0.343295</td>\n",
       "      <td>-2.045412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>757 rows × 121 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 RPI   W875RX1  DPCERA3M086SBEA  CMRMTSPLx   RETAILx  \\\n",
       "sasdate                                                                \n",
       "1960-01-02  0.319409  0.463825         0.278847   1.695646  2.660551   \n",
       "1960-01-03  0.114254  0.091674         0.433454   1.437148  0.369645   \n",
       "1960-01-04  0.190876  0.091590         1.404092  -2.802612 -0.110199   \n",
       "1960-01-05  0.341874  0.361379         1.535528   0.982647  2.590339   \n",
       "1960-01-06  0.240054  0.244332        -2.034337  -3.153052 -1.504683   \n",
       "...              ...       ...              ...        ...       ...   \n",
       "2022-01-12  0.000898 -0.075962        -0.414455  -0.947270 -1.320097   \n",
       "2023-01-01 -0.013781 -0.019171        -0.193846   1.490076 -0.719328   \n",
       "2023-01-02  0.087570  0.130699         1.316033   0.190398  2.764501   \n",
       "2023-01-03  0.103913 -0.004103        -0.028459  -0.417590 -0.660515   \n",
       "2023-01-04  0.259530  0.284085        -0.033085  -0.945104 -0.915446   \n",
       "\n",
       "              INDPRO   IPFPNSS   IPFINAL   IPCONGD   IPDCONGD  ...     PCEPI  \\\n",
       "sasdate                                                        ...             \n",
       "1960-01-02  2.591713  2.409495  2.902087  3.123538  10.383083  ... -0.171620   \n",
       "1960-01-03 -0.893885 -0.568540 -0.343503 -1.145653  -1.385485  ...  0.159303   \n",
       "1960-01-04 -0.901948 -0.342682 -0.115042  0.115277  -1.996461  ... -0.049056   \n",
       "1960-01-05 -0.795880  0.228983  0.115042  0.687998  -0.118913  ...  0.311430   \n",
       "1960-01-06 -0.114277  0.568790  0.686244  0.569884   0.945486  ... -0.262920   \n",
       "...              ...       ...       ...       ...        ...  ...       ...   \n",
       "2022-01-12 -0.330005 -0.332552 -0.400833 -0.179349  -2.240469  ... -0.248867   \n",
       "2023-01-01 -1.550613 -1.202485 -0.846966 -0.623823  -1.031771  ...  0.030879   \n",
       "2023-01-02  1.003181  0.343776 -0.014901 -0.334291   0.217564  ...  0.382455   \n",
       "2023-01-03 -0.005561 -0.196666 -0.129562  0.097723   0.212525  ... -0.293685   \n",
       "2023-01-04  0.146036 -0.076300 -0.030143  0.276299  -1.061013  ... -0.175550   \n",
       "\n",
       "            DDURRG3M086SBEA  DNDGRG3M086SBEA  DSERRG3M086SBEA  CES0600000008  \\\n",
       "sasdate                                                                        \n",
       "1960-01-02        -0.259079        -0.178584        -0.128845      -0.468036   \n",
       "1960-01-03         0.376462         0.167438         0.085463      -0.455545   \n",
       "1960-01-04        -0.455511         0.178561        -0.111313      -0.002011   \n",
       "1960-01-05         0.411911         0.443860         0.145034      -0.894856   \n",
       "1960-01-06        -0.129782        -0.672338         0.059133       0.894856   \n",
       "...                     ...              ...              ...            ...   \n",
       "2022-01-12        -0.174864        -0.884680        -0.056635       0.102595   \n",
       "2023-01-01         0.344041        -0.613838         0.175427      -0.107940   \n",
       "2023-01-02         0.474433         1.425376         0.033243       0.170724   \n",
       "2023-01-03        -0.436099        -0.416042        -0.224790      -0.417543   \n",
       "2023-01-04         0.100107        -0.645568        -0.081575       0.240091   \n",
       "\n",
       "            CES2000000008  CES3000000008  DTCOLNVHFNM  DTCTHFNM    INVEST  \n",
       "sasdate                                                                    \n",
       "1960-01-02       0.779728      -0.970531     0.429236 -1.173886 -1.333027  \n",
       "1960-01-03       0.385334      -0.475018     0.826845  0.538766 -1.894229  \n",
       "1960-01-04       3.013134      -0.468385     0.347209  0.095090  0.348999  \n",
       "1960-01-05      -6.807429       0.000000     0.867818  0.545173  2.413598  \n",
       "1960-01-06       4.161967       0.000000    -0.482728 -0.206712  0.379810  \n",
       "...                   ...            ...          ...       ...       ...  \n",
       "2022-01-12      -0.062401       0.389154     0.162379 -0.007056  0.959749  \n",
       "2023-01-01       0.300155      -0.590025    -0.283245 -0.123254  0.880929  \n",
       "2023-01-02      -0.004386       0.309575     0.071035  0.049043 -0.142513  \n",
       "2023-01-03      -0.152983      -0.621376    -0.237727 -0.068985 -0.121427  \n",
       "2023-01-04       0.027041       0.579697     0.505030  0.343295 -2.045412  \n",
       "\n",
       "[757 rows x 121 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c41b1e1d-ba14-4d6b-882f-b7482fdd76ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sasdate\n",
       "1960-01-02   -0.02\n",
       "1960-01-03   -0.13\n",
       "1960-01-04    0.08\n",
       "1960-01-05   -0.07\n",
       "1960-01-06   -0.53\n",
       "              ... \n",
       "2022-01-12    0.32\n",
       "2023-01-01    0.23\n",
       "2023-01-02    0.24\n",
       "2023-01-03    0.08\n",
       "2023-01-04    0.18\n",
       "Name: FEDFUNDS, Length: 757, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a96d2e5-87da-4ed7-9045-2b87b02898b4",
   "metadata": {},
   "source": [
    "## Dimensionality Reduction: By Selecting KBest Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "061f65c8-f92d-42a3-8a03-34a05b5ce9ab",
   "metadata": {},
   "source": [
    "In this section, we reduce the dimensionality of the input features by the method of selecting k(ten) best features , ensuring that the majority of the data variance was preserved while simplifying the model's input space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "04475aaf-849c-47d1-9548-de745e3c9143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False,  True, False, False,  True,\n",
       "        True, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False,  True,  True,  True,  True,  True,  True,\n",
       "        True, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_KBest = SelectKBest(score_func=f_regression, k=10)\n",
    "\n",
    "model_KBest = model_KBest.fit(X, y)\n",
    "\n",
    "KBest = model_KBest.get_support()\n",
    "KBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "75b49275-7ffe-4dc9-9e5d-baf913f9c8c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['USGOOD',\n",
       " 'MANEMP',\n",
       " 'DMANEMP',\n",
       " 'CP3Mx',\n",
       " 'TB3MS',\n",
       " 'TB6MS',\n",
       " 'GS1',\n",
       " 'GS5',\n",
       " 'GS10',\n",
       " 'AAA']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KBest_feature = []\n",
    "\n",
    "for i in range(0,KBest.shape[0]):\n",
    "    if KBest[i] == True:\n",
    "        K_feature = X.columns[i]\n",
    "        KBest_feature.append(K_feature)\n",
    "    \n",
    "KBest_feature       "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e136ef-515a-48bd-a21b-d8a9708f5437",
   "metadata": {},
   "source": [
    "## SVR Regrassion: Define Training, Validation, and Test Sets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a076f9a1-7fa9-4b68-88b0-15c0c257a3b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "529 113 115\n"
     ]
    }
   ],
   "source": [
    "train_pct = 0.7\n",
    "valid_pct = 0.15\n",
    "test_pct = 0.15\n",
    "\n",
    "n_train = int(train_pct*len(X))\n",
    "n_valid = int(valid_pct*len(X))\n",
    "n_test = len(X)-n_train-n_valid\n",
    "\n",
    "print(n_train, n_valid, n_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7444cc61-e796-4b4b-8d09-bbedf781e314",
   "metadata": {},
   "source": [
    "## Grid Search for Optimal Hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8efabd78-dfa9-4fff-933b-78a1cd67680d",
   "metadata": {},
   "source": [
    "The Grid Search was mainly adapted from the code provided by Professor C-Y (Eric) NG in the Econ 5060 course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7e42de90-a408-4461-b91c-e8e86fbac948",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pipeline(config):\n",
    "\n",
    "    # unpack config\n",
    "    n_features = config\n",
    "\n",
    "    # Steps\n",
    "    steps = [('SelectKBest', SelectKBest(score_func=f_regression, k=n_features)),\n",
    "             ('rbf', SVR())\n",
    "            ]\n",
    "\n",
    "    pipeline = Pipeline(steps)\n",
    "\n",
    "    return pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4479d3d7-93cd-4ad3-bc6b-76db6f9fbb97",
   "metadata": {},
   "outputs": [],
   "source": [
    "def walk_forward_validation(cfg):\n",
    "    \n",
    "    n_trains = n_train\n",
    "\n",
    "    n_records = n_train + n_valid\n",
    "\n",
    "    y_test_list = []\n",
    "    \n",
    "    y_pred_list = []\n",
    "\n",
    "    j = 0\n",
    "\n",
    "    for i in range(n_trains, n_records):\n",
    "    \n",
    "        X_train, X_test, y_train, y_test = X[j:i], X[i:i+1], y[j:i], y[i:i+1]\n",
    "       \n",
    "        model = pipeline(cfg).fit(X_train, y_train)        \n",
    "    \n",
    "        y_pred = model.predict(X_test)\n",
    "    \n",
    "        y_pred_list.extend(y_pred)\n",
    "    \n",
    "        y_test_list.extend(y_test)\n",
    "\n",
    "        j += 1\n",
    "    \n",
    "    score_rmse = metrics.mean_squared_error(y_test_list, y_pred_list, squared=False)\n",
    "    \n",
    "    print(' > %.3f' % score_rmse)\n",
    "    \n",
    "    return score_rmse "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6ba6fbf7-d883-4fa0-959e-c7c881fc5855",
   "metadata": {},
   "outputs": [],
   "source": [
    "# score a model, return None on failure\n",
    "def repeat_evaluate(config, n_repeats=1):\n",
    "    # convert config to a key\n",
    "    key = str(config)\n",
    "    # fit and evaluate the model n times\n",
    "    scores = [walk_forward_validation(config) for _ in range(n_repeats)]\n",
    "    # summarize score\n",
    "    result = np.mean(scores)\n",
    "    print('> Model[%s] %.3f' % (key, result))\n",
    "    return (key, result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b128dbd1-1387-46c2-ab90-2aeb1070c3d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# grid search configs\n",
    "def grid_search(cfg_list):\n",
    "    # evaluate configs\n",
    "    scores = [repeat_evaluate(cfg) for cfg in cfg_list]\n",
    "    # sort configs by score_rmse, asc\n",
    "    #scores.sort(key=lambda tup: tup[1])\n",
    "    scores.sort(key=lambda tup: tup[1], reverse=True)\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "bb37a399-b932-4592-a1a8-4cae66af89b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a list of configs to try\n",
    "def model_configs():\n",
    "    # define scope of configs\n",
    "      \n",
    "    n_features = [4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50]\n",
    "    #n_features = [10]\n",
    "     \n",
    "    # create configs\n",
    "    configs = list()\n",
    "    \n",
    "    for k in n_features:\n",
    "        cfg = k\n",
    "        configs.append(cfg)\n",
    "\n",
    "    print('Total configs: %d' % len(configs))\n",
    "    return configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b3eb4d0d-231f-4375-9811-7c7fd7d0c4e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total configs: 24\n"
     ]
    }
   ],
   "source": [
    "cfg_list = model_configs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c824e62b-d76c-474a-9114-49dd6716f841",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total configs: 24\n",
      " > 0.135\n",
      "> Model[4] 0.135\n",
      " > 0.136\n",
      "> Model[6] 0.136\n",
      " > 0.154\n",
      "> Model[8] 0.154\n",
      " > 0.171\n",
      "> Model[10] 0.171\n",
      " > 0.168\n",
      "> Model[12] 0.168\n",
      " > 0.183\n",
      "> Model[14] 0.183\n",
      " > 0.172\n",
      "> Model[16] 0.172\n",
      " > 0.159\n",
      "> Model[18] 0.159\n",
      " > 0.190\n",
      "> Model[20] 0.190\n",
      " > 0.195\n",
      "> Model[22] 0.195\n",
      " > 0.189\n",
      "> Model[24] 0.189\n",
      " > 0.202\n",
      "> Model[26] 0.202\n",
      " > 0.203\n",
      "> Model[28] 0.203\n",
      " > 0.202\n",
      "> Model[30] 0.202\n",
      " > 0.201\n",
      "> Model[32] 0.201\n",
      " > 0.201\n",
      "> Model[34] 0.201\n",
      " > 0.201\n",
      "> Model[36] 0.201\n",
      " > 0.200\n",
      "> Model[38] 0.200\n",
      " > 0.200\n",
      "> Model[40] 0.200\n",
      " > 0.196\n",
      "> Model[42] 0.196\n",
      " > 0.196\n",
      "> Model[44] 0.196\n",
      " > 0.196\n",
      "> Model[46] 0.196\n",
      " > 0.199\n",
      "> Model[48] 0.199\n",
      " > 0.198\n",
      "> Model[50] 0.198\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "cfg_list = model_configs()\n",
    "# grid search\n",
    "scores = grid_search(cfg_list)\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4ad429a5-3f28-4167-93aa-4669570a26f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('28', 0.20276046309984525),\n",
       " ('26', 0.2024320448125936),\n",
       " ('30', 0.20241280502662537),\n",
       " ('32', 0.20073963701919584),\n",
       " ('34', 0.2007160373112864),\n",
       " ('36', 0.200676896551442),\n",
       " ('38', 0.20046564069196626),\n",
       " ('40', 0.20036390981404395),\n",
       " ('48', 0.19902951388393908),\n",
       " ('50', 0.19831457564068997),\n",
       " ('46', 0.1957759135652771),\n",
       " ('42', 0.19568356546800966),\n",
       " ('44', 0.19563844560843943),\n",
       " ('22', 0.19531334023231503),\n",
       " ('20', 0.19042516365838041),\n",
       " ('24', 0.18864181319174372),\n",
       " ('14', 0.1831809041693067),\n",
       " ('16', 0.17213007708877553),\n",
       " ('10', 0.1710802066479259),\n",
       " ('12', 0.1684970740209361),\n",
       " ('18', 0.15929904144982845),\n",
       " ('8', 0.15388712432584187),\n",
       " ('6', 0.13637861064457726),\n",
       " ('4', 0.1353142198178233)]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "289f889a-4007-4dda-bed3-3d3b5b031515",
   "metadata": {},
   "source": [
    "## Final Model Estimation & Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "cf96f676-6b98-4a9e-b967-bdbf8a7caf52",
   "metadata": {},
   "outputs": [],
   "source": [
    "steps_final = [('SelectKBest', SelectKBest(score_func=f_regression, k=26)),\n",
    "                   ('rbf', SVR())\n",
    "              ]\n",
    "\n",
    "pipeline_final = Pipeline(steps_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "928b8be2-84a1-47aa-967a-d142dde26048",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Time: 2.331099510192871\n"
     ]
    }
   ],
   "source": [
    "start=time.time()    \n",
    "   \n",
    "n_trains = n_train + n_valid\n",
    "\n",
    "n_records = n_train + n_valid + n_test\n",
    "\n",
    "y_test_list = []\n",
    "    \n",
    "y_pred_list = []\n",
    "\n",
    "j = 0\n",
    "\n",
    "for i in range(n_trains, n_records):\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = X[j:i], X[i:i+1], y[j:i], y[i:i+1]\n",
    "        \n",
    "    model = pipeline_final.fit(X_train, y_train)            \n",
    "\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    y_pred_list.extend(y_pred)\n",
    "    \n",
    "    y_test_list.extend(y_test)\n",
    "\n",
    "    j += 1\n",
    "    \n",
    "end=time.time()\n",
    "\n",
    "print(\"Running Time:\", end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b1d94c64-dfa2-47ca-b1ce-770714ac0e7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FEDFUNDS_Actual</th>\n",
       "      <th>FEDFUNDS_Predicted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sasdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-08</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.019557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-09</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.060725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-10</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.072080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-11</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.059217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-12</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.062474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.052167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.087154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.006474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.053767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.070146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.036356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.076731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.053511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.000667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.094843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-11</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.070052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.055904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.082456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.072023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.054920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.069901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.086279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.041449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.008610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.073744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.042236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-10</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.077690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-11</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.038195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-12</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.012292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.055942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.072292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.022804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.036807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.028318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.028570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.000580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.083014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-09</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.027175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-10</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.042956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.083202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.068873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.152000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.008510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.048200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.155101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.032734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.008007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.104540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.025929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.045000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.128474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.033885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-12</th>\n",
       "      <td>0.14</td>\n",
       "      <td>0.122966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.121877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.110876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.103724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.189891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.068389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.085753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-07</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.097194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.116601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.121507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.064299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.120435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-12</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.166486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.021725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.062253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.059586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.037755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.079839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.002458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.096353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>-0.27</td>\n",
       "      <td>0.049900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-09</th>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.042865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-10</th>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.053094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-11</th>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.326758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-12</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.016498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.039161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.060375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.004475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.246731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.233909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.213390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.082973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-10</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.124408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-11</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.126889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-12</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.101728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.015269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-02</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.063367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-03</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.247969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.127074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.007965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-06</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.080949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-07</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.054170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-08</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.029385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-09</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.088214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-10</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.022346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-11</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.105118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-12</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.060894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.136980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-02</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.044699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.078079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.041466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0.120409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-06</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0.121958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-07</th>\n",
       "      <td>0.47</td>\n",
       "      <td>0.372314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-08</th>\n",
       "      <td>0.65</td>\n",
       "      <td>0.305619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-09</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.254457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-10</th>\n",
       "      <td>0.52</td>\n",
       "      <td>0.382608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-11</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.386041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-12</th>\n",
       "      <td>0.32</td>\n",
       "      <td>0.332139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.073420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-02</th>\n",
       "      <td>0.24</td>\n",
       "      <td>-0.029994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-03</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.125174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-04</th>\n",
       "      <td>0.18</td>\n",
       "      <td>-0.016724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            FEDFUNDS_Actual  FEDFUNDS_Predicted\n",
       "sasdate                                        \n",
       "2013-01-08            -0.01           -0.019557\n",
       "2013-01-09             0.00            0.060725\n",
       "2013-01-10             0.01            0.072080\n",
       "2013-01-11            -0.01            0.059217\n",
       "2013-01-12             0.01            0.062474\n",
       "2014-01-01            -0.02            0.052167\n",
       "2014-01-02             0.00            0.087154\n",
       "2014-01-03             0.01           -0.006474\n",
       "2014-01-04             0.01            0.053767\n",
       "2014-01-05             0.00            0.070146\n",
       "2014-01-06             0.01            0.036356\n",
       "2014-01-07            -0.01            0.076731\n",
       "2014-01-08             0.00           -0.053511\n",
       "2014-01-09             0.00           -0.000667\n",
       "2014-01-10             0.00            0.094843\n",
       "2014-01-11             0.00            0.070052\n",
       "2014-01-12             0.03            0.055904\n",
       "2015-01-01            -0.01            0.082456\n",
       "2015-01-02             0.00           -0.072023\n",
       "2015-01-03             0.00           -0.054920\n",
       "2015-01-04             0.01            0.069901\n",
       "2015-01-05             0.00           -0.086279\n",
       "2015-01-06             0.01            0.041449\n",
       "2015-01-07             0.00           -0.008610\n",
       "2015-01-08             0.01            0.073744\n",
       "2015-01-09             0.00            0.042236\n",
       "2015-01-10            -0.02           -0.077690\n",
       "2015-01-11             0.00           -0.038195\n",
       "2015-01-12             0.12            0.012292\n",
       "2016-01-01             0.10            0.055942\n",
       "2016-01-02             0.04            0.072292\n",
       "2016-01-03            -0.02            0.022804\n",
       "2016-01-04             0.01            0.036807\n",
       "2016-01-05             0.00            0.028318\n",
       "2016-01-06             0.01            0.028570\n",
       "2016-01-07             0.01           -0.000580\n",
       "2016-01-08             0.01            0.083014\n",
       "2016-01-09             0.00            0.027175\n",
       "2016-01-10             0.00            0.042956\n",
       "2016-01-11             0.01            0.083202\n",
       "2016-01-12             0.13            0.068873\n",
       "2017-01-01             0.11            0.152000\n",
       "2017-01-02             0.01            0.008510\n",
       "2017-01-03             0.13            0.048200\n",
       "2017-01-04             0.11            0.155101\n",
       "2017-01-05             0.01           -0.032734\n",
       "2017-01-06             0.13            0.008007\n",
       "2017-01-07             0.11            0.104540\n",
       "2017-01-08             0.01            0.025929\n",
       "2017-01-09            -0.01            0.045000\n",
       "2017-01-10             0.00            0.128474\n",
       "2017-01-11             0.01            0.033885\n",
       "2017-01-12             0.14            0.122966\n",
       "2018-01-01             0.11            0.121877\n",
       "2018-01-02             0.01            0.110876\n",
       "2018-01-03             0.09            0.103724\n",
       "2018-01-04             0.18            0.189891\n",
       "2018-01-05             0.01            0.068389\n",
       "2018-01-06             0.12           -0.085753\n",
       "2018-01-07             0.09            0.097194\n",
       "2018-01-08             0.00            0.116601\n",
       "2018-01-09             0.04            0.121507\n",
       "2018-01-10             0.24            0.064299\n",
       "2018-01-11             0.01            0.120435\n",
       "2018-01-12             0.07            0.166486\n",
       "2019-01-01             0.13            0.021725\n",
       "2019-01-02             0.00           -0.062253\n",
       "2019-01-03             0.01           -0.059586\n",
       "2019-01-04             0.01            0.037755\n",
       "2019-01-05            -0.03           -0.079839\n",
       "2019-01-06            -0.01            0.002458\n",
       "2019-01-07             0.02           -0.096353\n",
       "2019-01-08            -0.27            0.049900\n",
       "2019-01-09            -0.09           -0.042865\n",
       "2019-01-10            -0.21           -0.053094\n",
       "2019-01-11            -0.28           -0.326758\n",
       "2019-01-12             0.00           -0.016498\n",
       "2020-01-01             0.00           -0.039161\n",
       "2020-01-02             0.03           -0.060375\n",
       "2020-01-03            -0.93           -0.004475\n",
       "2020-01-04            -0.60           -0.246731\n",
       "2020-01-07             0.01           -0.233909\n",
       "2020-01-08             0.01           -0.213390\n",
       "2020-01-09            -0.01            0.082973\n",
       "2020-01-10             0.00            0.124408\n",
       "2020-01-11             0.00            0.126889\n",
       "2020-01-12             0.00            0.101728\n",
       "2021-01-01             0.00            0.015269\n",
       "2021-01-02            -0.01            0.063367\n",
       "2021-01-03            -0.01           -0.247969\n",
       "2021-01-04             0.00            0.127074\n",
       "2021-01-05            -0.01           -0.007965\n",
       "2021-01-06             0.02            0.080949\n",
       "2021-01-07             0.02            0.054170\n",
       "2021-01-08            -0.01            0.029385\n",
       "2021-01-09            -0.01            0.088214\n",
       "2021-01-10             0.00           -0.022346\n",
       "2021-01-11             0.00            0.105118\n",
       "2021-01-12             0.00            0.060894\n",
       "2022-01-01             0.00            0.136980\n",
       "2022-01-02             0.00            0.044699\n",
       "2022-01-03             0.12            0.078079\n",
       "2022-01-04             0.13            0.041466\n",
       "2022-01-05             0.44            0.120409\n",
       "2022-01-06             0.44            0.121958\n",
       "2022-01-07             0.47            0.372314\n",
       "2022-01-08             0.65            0.305619\n",
       "2022-01-09             0.23            0.254457\n",
       "2022-01-10             0.52            0.382608\n",
       "2022-01-11             0.70            0.386041\n",
       "2022-01-12             0.32            0.332139\n",
       "2023-01-01             0.23            0.073420\n",
       "2023-01-02             0.24           -0.029994\n",
       "2023-01-03             0.08            0.125174\n",
       "2023-01-04             0.18           -0.016724"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_rows=300\n",
    "result = pd.DataFrame({'FEDFUNDS_Actual': y[-n_test:], 'FEDFUNDS_Predicted': y_pred_list}, columns=['FEDFUNDS_Actual', 'FEDFUNDS_Predicted'])\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d155fae0-e817-4675-acff-0e0007907476",
   "metadata": {},
   "outputs": [],
   "source": [
    "final = result.iloc[[0, 12, 24, 36,48,60,72,84,96,108,112]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f5de1ac9-a4a7-4cee-9ee3-fec361d0ea4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FEDFUNDS_Actual</th>\n",
       "      <th>FEDFUNDS_Predicted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sasdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-08</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.019557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.053511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.073744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.083014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.025929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.116601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>-0.27</td>\n",
       "      <td>0.049900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-10</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.124408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-10</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.022346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-10</th>\n",
       "      <td>0.52</td>\n",
       "      <td>0.382608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-02</th>\n",
       "      <td>0.24</td>\n",
       "      <td>-0.029994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            FEDFUNDS_Actual  FEDFUNDS_Predicted\n",
       "sasdate                                        \n",
       "2013-01-08            -0.01           -0.019557\n",
       "2014-01-08             0.00           -0.053511\n",
       "2015-01-08             0.01            0.073744\n",
       "2016-01-08             0.01            0.083014\n",
       "2017-01-08             0.01            0.025929\n",
       "2018-01-08             0.00            0.116601\n",
       "2019-01-08            -0.27            0.049900\n",
       "2020-01-10             0.00            0.124408\n",
       "2021-01-10             0.00           -0.022346\n",
       "2022-01-10             0.52            0.382608\n",
       "2023-01-02             0.24           -0.029994"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "56f27ae0-4ff3-4824-a4fe-f2d4ff356456",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sasdate'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final['FEDFUNDS_Actual'].plot(legend=True)\n",
    "final['FEDFUNDS_Predicted'].plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "995a1c7d-689e-4f4d-9fbb-5b4ea0ef5356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d17a4f8790>]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test_list, y_pred_list)\n",
    "plt.plot(y_test_list, y_test_list,'k-') # identity line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f64663ef-69ef-47a5-b3d0-31945dcce194",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.039\n",
      "Root Mean Squared Error: 0.197\n",
      "Mean Absolute Error: 0.197\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = mse**0.5 \n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "\n",
    "print(f'Mean Squared Error: {mse:.3f}')\n",
    "print(f'Root Mean Squared Error: {rmse:.3f}')\n",
    "print(f'Mean Absolute Error: {mae:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d7572eed-e85d-40ae-bda2-d8a1bdf93beb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FEDFUNDS_Actual</th>\n",
       "      <th>FEDFUNDS_Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FEDFUNDS_Actual</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.597186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEDFUNDS_Predicted</th>\n",
       "      <td>0.597186</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    FEDFUNDS_Actual  FEDFUNDS_Predicted\n",
       "FEDFUNDS_Actual            1.000000            0.597186\n",
       "FEDFUNDS_Predicted         0.597186            1.000000"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "081a68ad-68f8-4577-9246-888e88f71594",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ad91f3-fae0-44a1-a97e-f5f4fc8d60e3",
   "metadata": {},
   "source": [
    "The RMSE and MAE of the model shown above are both about 0.197 and the correlation coefficient between actual FEDFUNDS and FEFUNDS predicted is 0.5972, reflecting small prediction errors in absolute terms. These results suggest that the model performs not bad in terms of minimizing numerical deviations between predicted and actual values and giving a reliable prediction, which is critical for financial forecasting tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f9f3f2a-4957-420e-b249-b5d8c4ff05cc",
   "metadata": {},
   "source": [
    "## Reference"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ef810c-fb68-4b37-91a7-313f8a1e91d9",
   "metadata": {},
   "source": [
    "1. LIBSVM: A Library for Support Vector Machines\n",
    "2. Platt, John (1999). “Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods”\n",
    "3. Harsh S. Dhiman, Dipankar Deb, Josep M. Guerrero,Hybrid machine intelligent SVR variants for wind forecasting and ramp events,Renewable and Sustainable Energy Reviews.\n",
    "4. An improved grid search algorithm to optimize SVR for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4cd70a6-4f2a-49b5-8197-e770aa58c1e8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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