Time Series Forecasting

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.

Prerequisites

Before contributing or adding a new feature, Please make sure you have already installed the following tools:

• Git
• Python

Installation Steps

1. Download Dataset from here --> Dataset

2. download main.ipynb file

3. Create a Conda environment:

   conda create --name myenv
   

4. Activate the environment:

   • For Windows:

     conda activate myenv
     

   • For macOS/Linux:

     source activate myenv
     

5. Install dependencies:

   conda install <package_name>
   

6. Install packages using pip (if not available in conda):

   pip install <package_name>
   

7. Run Jupyter Notebook:

   jupyter notebook
   

Contributors

Managed by Ranjit Odedra