Time Series Forecasting for Financial Markets: This project involved the development of a robust time series forecasting model for Yahoo stock prices. We employed a comprehensive time series decomposition analysis to break down the stock price data into its constituent components, namely trend, seasonality, and residuals. Through insightful visualizations, we gained a deep understanding of how these components interacted over time, providing valuable insights into the stock's historical behavior. This analysis serves as a crucial foundation for making informed predictions and decisions in the dynamic world of financial markets.
Before contributing or adding a new feature, Please make sure you have already installed the following tools:
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Download Dataset from here --> Dataset
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download main.ipynb file
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Create a Conda environment:
conda create --name myenv -
Activate the environment:
- For Windows:
conda activate myenv - For macOS/Linux:
source activate myenv
- For Windows:
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Install dependencies:
conda install <package_name> -
Install packages using pip (if not available in conda):
pip install <package_name> -
Run Jupyter Notebook:
jupyter notebook
Managed by Ranjit Odedra