MultivariateSeasonalTimeseriesPredictionPCA.ipynb

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        "<a href=\"https://colab.research.google.com/github/ptleskin/MultivariateSeasonalTimeseriesPredictionPCA/blob/master/MultivariateSeasonalTimeseriesPredictionPCA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DVCvfAKvN8dg",
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      "source": [
        "Install dependencies"
      ]
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      "source": [
        "!pip install numpy sklearn pandas matplotlib"
      ],
      "execution_count": 0,
      "outputs": [
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          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (1.14.6)\n",
            "Requirement already satisfied: sklearn in /usr/local/lib/python3.6/dist-packages (0.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (0.22.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (3.0.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from sklearn) (0.20.2)\n",
            "Requirement already satisfied: python-dateutil>=2 in /usr/local/lib/python3.6/dist-packages (from pandas) (2.5.3)\n",
            "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas) (2018.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (0.10.0)\n",
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            "Requirement already satisfied: scipy>=0.13.3 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (1.1.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2->pandas) (1.11.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib) (40.7.3)\n"
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      "cell_type": "markdown",
      "metadata": {
        "id": "tktJjT8TN8eT",
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      "source": [
        "Import libraries"
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a6N87O1kN8eY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from datetime import datetime\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.svm import SVR"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yUsrrb_vN8eh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "TRAIN_CSV = \"https://raw.githubusercontent.com/ptleskin/exerciseData/master/data/training_set.csv\"\n",
        "TEST_CSV = \"https://raw.githubusercontent.com/ptleskin/exerciseData/master/data/test_set.csv\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4MutQWT9N8eq",
        "colab_type": "text"
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      "source": [
        "Read training data:"
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        "outputId": "6fe58e14-ab6d-4312-95ae-df5cbac73e82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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      },
      "source": [
        "n_keys = 11 # number of keywords\n",
        "sources = ['x_{}'.format(i) for i in range(n_keys)]\n",
        "\n",
        "df = pd.read_csv(TRAIN_CSV, sep=';', header=None, names=['ds'] + sources + ['y'])\n",
        "\n",
        "# save the timestamps in ordinal format for later use:\n",
        "t_train = [datetime.strptime(st, '%Y-%m-%d %H:%M:%S').toordinal() for st in df['ds']]\n",
        "\n",
        "df.tail()"
      ],
      "execution_count": 0,
      "outputs": [
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              "                      ds   x_0  x_1  x_2  x_3   x_4  x_5  x_6   x_7   x_8  \\\n",
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              "\n",
              "      x_9  x_10   y  \n",
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      "metadata": {
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      "source": [
        "Read test data:"
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    {
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        "colab": {
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      "source": [
        "df2 = pd.read_csv(TEST_CSV, sep=';', header=None, names = ['ds'] + sources)\n",
        "\n",
        "# save the timestamps in ordinal format for later use:\n",
        "t_test = [datetime.strptime(st, '%Y-%m-%d %H:%M:%S').toordinal() for st in df2['ds']]\n",
        "\n",
        "df2.head()"
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      "execution_count": 0,
      "outputs": [
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              "      <th>1</th>\n",
              "      <td>2014-06-09 00:00:00</td>\n",
              "      <td>10.0</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>7.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>2014-06-16 00:00:00</td>\n",
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              "      <td>1.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>2014-06-23 00:00:00</td>\n",
              "      <td>28.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
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              "      <td>8.0</td>\n",
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              "      <td>9.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>2014-06-30 00:00:00</td>\n",
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              "                    ds   x_0  x_1  x_2  x_3   x_4  x_5  x_6   x_7   x_8  x_9  \\\n",
              "0  2014-06-02 00:00:00   9.0  2.0  2.0  0.0  11.0  1.0  0.0  10.0  15.0  6.0   \n",
              "1  2014-06-09 00:00:00  10.0  1.0  3.0  4.0  16.0  0.0  0.0   7.0  46.0  2.0   \n",
              "2  2014-06-16 00:00:00   6.0  2.0  0.0  1.0  10.0  0.0  0.0   8.0  20.0  1.0   \n",
              "3  2014-06-23 00:00:00  28.0  1.0  4.0  2.0   8.0  0.0  0.0   5.0  17.0  9.0   \n",
              "4  2014-06-30 00:00:00   8.0  4.0  0.0  1.0  13.0  0.0  0.0   3.0  12.0  5.0   \n",
              "\n",
              "   x_10  \n",
              "0   1.0  \n",
              "1   4.0  \n",
              "2   4.0  \n",
              "3   3.0  \n",
              "4   7.0  "
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YZfEyvNIN8fQ",
        "colab_type": "text"
      },
      "source": [
        "Notice that time stamps of the training data continue in the test data.\n",
        "\n",
        "Study the data by plotting it:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3PBqjV2pN8gS",
        "colab_type": "code",
        "outputId": "e0588b9b-0155-47df-c0cf-7b129acc8aed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(df['y'].values)\n",
        "ax.grid()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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wXIolxhy/CMgBmRkyEZGaDBmQX3nlFZx77rmYNm1a3K8nOsCABxuMH6WoS8MpayIiNdMN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\nr5g/HKqyN9UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4KZaAZoN8gy",
        "colab_type": "text"
      },
      "source": [
        "So we are dealing with a relatively small data set with periodical behaviour."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nf2CMFuaN8g4",
        "colab_type": "text"
      },
      "source": [
        "Now plot the keyword counts:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hapd8o6WN8g8",
        "colab_type": "code",
        "outputId": "18a1b7a9-cf05-4f35-8185-568edebe3278",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "fig, ax = plt.subplots(len(sources), 1, sharex='col', sharey='row')\n",
        "\n",
        "for i,s in enumerate(sources):\n",
        "    ax[i].plot(df[s].values)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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srYkq+Z/ZWkFFUSpLSzNweYPcsWE+W1YWord6uPPaEhYUpnLb+mLu2DCfVQuz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\n2xt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            "text/plain": [
              "<Figure size 576x396 with 11 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1fgfCk8N8hG",
        "colab_type": "text"
      },
      "source": [
        "Let's take a look at the correlation coefficients, to see how much mutual information there is in the keyword data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_PXk_U0N8hJ",
        "colab_type": "code",
        "outputId": "6ffac068-35ba-4dcb-a2d1-111c61805d59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "source": [
        "pd.DataFrame(data = np.corrcoef(df[sources].values, rowvar = False))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
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            ],
            "text/plain": [
              "          0         1         2         3         4         5         6   \\\n",
              "0   1.000000  0.165659  0.240735  0.261566  0.323946  0.148247  0.109507   \n",
              "1   0.165659  1.000000  0.188686  0.126909  0.234532  0.170091  0.104815   \n",
              "2   0.240735  0.188686  1.000000  0.638754  0.870728  0.381759  0.282963   \n",
              "3   0.261566  0.126909  0.638754  1.000000  0.665850  0.362167  0.251680   \n",
              "4   0.323946  0.234532  0.870728  0.665850  1.000000  0.479626  0.242197   \n",
              "5   0.148247  0.170091  0.381759  0.362167  0.479626  1.000000  0.257780   \n",
              "6   0.109507  0.104815  0.282963  0.251680  0.242197  0.257780  1.000000   \n",
              "7   0.406442  0.212833  0.603716  0.631225  0.730889  0.330405  0.279513   \n",
              "8   0.430403  0.240584  0.469311  0.525285  0.603969  0.284482  0.130701   \n",
              "9   0.247847  0.207836  0.893345  0.503034  0.813183  0.328191  0.244170   \n",
              "10  0.318768  0.227255  0.795883  0.656606  0.868653  0.402064  0.214234   \n",
              "\n",
              "          7         8         9         10  \n",
              "0   0.406442  0.430403  0.247847  0.318768  \n",
              "1   0.212833  0.240584  0.207836  0.227255  \n",
              "2   0.603716  0.469311  0.893345  0.795883  \n",
              "3   0.631225  0.525285  0.503034  0.656606  \n",
              "4   0.730889  0.603969  0.813183  0.868653  \n",
              "5   0.330405  0.284482  0.328191  0.402064  \n",
              "6   0.279513  0.130701  0.244170  0.214234  \n",
              "7   1.000000  0.677847  0.508777  0.716370  \n",
              "8   0.677847  1.000000  0.429498  0.612721  \n",
              "9   0.508777  0.429498  1.000000  0.731535  \n",
              "10  0.716370  0.612721  0.731535  1.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W7phoZI1N8hS",
        "colab_type": "text"
      },
      "source": [
        "There are many off-diagonal values in range 0.5 to 1.0, so the columns in the data are strongly correlated.\n",
        "\n",
        "Next we will apply dimensionality reduction with PCA:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vb5kYG1IN8hT",
        "colab_type": "code",
        "outputId": "fe108711-6593-4d9f-f4ac-1331966e958a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "source": [
        "n_components = 3\n",
        "pca = PCA(n_components=n_components)\n",
        "pca.fit(df[sources].values)\n",
        "\n",
        "X3 = pca.transform(df[sources].values)\n",
        "X3_test = pca.transform(df2[sources].values)\n",
        "\n",
        "fig, axs = plt.subplots(n_components, 1, sharex='col', sharey='row')\n",
        "\n",
        "pca_sources = ['p_{}'.format(i) for i in range(n_components)]\n",
        "\n",
        "if n_components==1:\n",
        "    axs.plot(X3[:,0])\n",
        "    df[pca_sources[0]] = X3[:,0]\n",
        "    df2[pca_sources[0]] = X3_test[:,0]\n",
        "else:\n",
        "    for i,ax in enumerate(axs):\n",
        "        ax.plot(X3[:,i])\n",
        "        df[pca_sources[i]] = X3[:,i]\n",
        "        df2[pca_sources[i]] = X3_test[:,i]\n",
        "\n",
        "plt.show()\n",
        "\n",
        "np.set_printoptions(precision=2)\n",
        "(pca.explained_variance_ratio_, np.sum(pca.explained_variance_ratio_))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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d5Gcb0WrU+AMh3t7TyisfNAFw82VlXHNhCZXNffzPc4cHLWQ5HvhOdOmSKXT2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\nHEALWURExAG0kEVERBxAC1lERMQBvgMLqDnEG1GUMAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0.86, 0.07, 0.03]), 0.947278311828126)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mq_637M1N8hh",
        "colab_type": "text"
      },
      "source": [
        "The first PCA component explain 86 per cent of the data, and would produce a feasible result when used in combination with the disease count data. However we will choose the three first components with 95 per cent value.\n",
        "\n",
        "Due to the small data size we chose to use a Support Vector Machine, and predict the next y-value by a longer sequence of earlier samples.\n",
        "\n",
        "In addition to the x[0], x[1], ... x[10] values, we will also use y[n-1], y[n-2], ... the predict next y[n].\n",
        "\n",
        "Initialize a Support Vector Machine for regression:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zIhWl2vMN8hi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clf = SVR(kernel='linear')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7y4wMSTEN8hr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_timesteps = 125 # number of time steps\n",
        "\n",
        "X, y = [], [] \n",
        "\n",
        "data = df[pca_sources+['y']].values\n",
        "\n",
        "for i in range(df.shape[0]-n_timesteps+1):\n",
        "    D = data[i:i+n_timesteps,:].flatten() # collect n_timesteps number of rows, and convert into 1D array\n",
        "    X.append(D[:-1]) # collect keyword counts into X, leave out the last number = y-value\n",
        "    y.append(D[-1])  # and disease counts into y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yCV2Bff9N8h1",
        "colab_type": "text"
      },
      "source": [
        "Fit the SVR:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLbuyN4qN8h3",
        "colab_type": "code",
        "outputId": "1034268f-5e1b-4afb-939d-ee33337911ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "clf.fit(X, y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
              "  gamma='auto_deprecated', kernel='linear', max_iter=-1, shrinking=True,\n",
              "  tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x_fDXopJN8iH",
        "colab_type": "text"
      },
      "source": [
        "Use the last y-value from the training data as a prior for future y-values:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wxuKZVCMN8iK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df2['y'] = y[-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fTt4-TYKN8iT",
        "colab_type": "text"
      },
      "source": [
        "Convert the data matrix into a format suitable for the regressor:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PhtO0EJFN8iX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataX = np.concatenate((data[1-n_timesteps:], df2[pca_sources + ['y']].values), axis=0)\n",
        "\n",
        "X2 = []\n",
        "for i in range(len(dataX)-n_timesteps+1):\n",
        "    D = dataX[i:i+n_timesteps,:].flatten()\n",
        "    X2.append(D[:-1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T3vpzQmDN8ig",
        "colab_type": "text"
      },
      "source": [
        "Predict the first estimate converted into non-negative integers:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h9BcxM0mN8ij",
        "colab_type": "code",
        "outputId": "de98578c-c94b-48a0-c892-6331226a5189",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "pred_0 = clf.predict(X2).astype('int')\n",
        "pred_0[pred_0<0] = 0\n",
        "pred_0"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  6,  3,  1,  0,  0,  5,  3,  4,  3,  6, 11, 16,  8,  7,  9,  3,\n",
              "        2,  1,  2,  1,  7,  7,  3,  5,  9,  6, 11, 13, 11,  7,  7, 10, 21,\n",
              "       27, 49, 65, 77, 72, 65, 56, 32, 25,  6,  5,  1, 10, 11,  8,  5,  4,\n",
              "       11])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BbK0xVSWN8i0",
        "colab_type": "text"
      },
      "source": [
        "Update the y-prior with the first prediction:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onZIcsyJN8i3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df2['y'] = pred_0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9z92afoN8i7",
        "colab_type": "text"
      },
      "source": [
        "Make the second prediction:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9JqSVb5JN8i-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataX = np.concatenate((data[1-n_timesteps:], df2[pca_sources + ['y']].values), axis=0)\n",
        "\n",
        "X2 = []\n",
        "for i in range(len(dataX)-n_timesteps+1):\n",
        "    D = dataX[i:i+n_timesteps,:].flatten()\n",
        "    X2.append(D[:-1])\n",
        "\n",
        "predictions = clf.predict(X2).astype('int')\n",
        "\n",
        "predictions[predictions<0] = 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4W4VhXP0N8jC",
        "colab_type": "text"
      },
      "source": [
        "Plot the data for visual evaluation:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJcE3zh1N8jF",
        "colab_type": "code",
        "outputId": "7d2726fa-80dc-4f61-8977-6da2025e97d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.plot(t_train, df['y'].values)\n",
        "ax.plot(t_test, predictions)\n",
        "\n",
        "ax.set_xticklabels([])\n",
        "ax.grid()\n",
        "ax.legend(['Existing data', 'Predicted'])\n",
        "plt.xlabel('Time series and prediction')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1Wrn22muZO3cumZmZp+N5GJIcvyiEEImj15L1hg0buOCCC0hLS8PpdPKrX/2KjRs3MmfO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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d-V8Oa7rScNx",
        "colab_type": "text"
      },
      "source": [
        "To have a closer look we will plot the last 50 values of the training data and the predicted sequence:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ykfAqyn4Q0GV",
        "colab_type": "code",
        "outputId": "5d5812b3-58ca-4b3c-a307-002c86b4e8d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.plot(t_train[-50:], df['y'].values[-50:])\n",
        "ax.plot(t_test, predictions)\n",
        "\n",
        "ax.set_xticklabels([])\n",
        "ax.grid()\n",
        "ax.legend(['Existing data', 'Predicted'])\n",
        "plt.xlabel('A closer look at time series and prediction')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T1kKI+JKALFKO0+PHoNOg1Rz74y/NQYQQiSIBWaQcl8ff43Q1SEAWQiSOBGSRctwe3zFb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\nmIH/B2bNprJVJcCyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gdo9VU21N8jY",
        "colab_type": "text"
      },
      "source": [
        "predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "flC5R2FCN8ja",
        "colab_type": "code",
        "outputId": "e12e80de-3b47-4f61-c49d-50fb0b74ffde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "predictions"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  6,  3,  2,  0,  0,  5,  3,  5,  3,  6, 11, 17,  9,  7,  9,  3,\n",
              "        3,  1,  3,  1,  6,  8,  4,  5,  8,  6, 10, 13, 12,  7,  6,  9, 21,\n",
              "       27, 50, 67, 82, 77, 69, 58, 33, 25,  5,  4,  0, 10, 11,  7,  4,  0,\n",
              "        6])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGzYEkBbN8jl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}