myekini / stock-analysis

This project performs an explanatory data and stock analysis on time series dataset

Stock Time Series Data Analysis Project

Welcome to the Stock Time Series Data Analysis Project! This project aims to perform Exploratory Data Analysis (EDA) and analyze time series data of stock prices. Below are instructions on how to run the project and an overview of the different analyses performed.

How to Run the Project:

1. Clone the repository: git clone https://github.com/myekini/EDA-and-Stock-analysis-with-time-series-data.git
2. Navigate to the project directory: cd Stock-Time-Series-Analysis
3. Install dependencies: pip install pandas numpy matplotlib
4. Run the project: python main.py

Analysis Performed:

1. Exploratory Data Analysis (EDA):

   • This analysis provides an initial overview of the stock time series data.
   • Key statistics and data visualizations are generated to understand trends, seasonality, and overall behavior.
   • EDA aids in identifying patterns and potential outliers in the historical stock prices.

2. Rolling Statistics:

   • This analysis computes rolling mean and rolling standard deviation to assess trends and volatility.
   • Visualizations of rolling statistics help to identify long-term trends in the stock data.

3. Seasonal Decomposition:

   • This analysis uses seasonal decomposition to separate the time series data into its trend, seasonal, and residual components.
   • By understanding these components, we can better comprehend the underlying patterns in stock price fluctuations.

4. Autocorrelation and Partial Autocorrelation:

   • This analysis examines autocorrelation and partial autocorrelation to determine the optimal parameters for time series models.
   • Insights from these correlations guide the selection of appropriate time series forecasting models.

5. Time Series Forecasting:

   • This analysis involves implementing time series forecasting models such as ARIMA or SARIMA for predicting future stock prices.
   • Forecasting helps in making informed decisions about potential investment strategies.

Contribution:

We welcome contributions to enhance the project and encourage you to participate! Here's how you can contribute:

1. Fork the repository.
2. Create a new branch: git checkout -b feature/your_feature.
3. Make changes and commit them: git commit -m "Add your changes".
4. Push the changes to your forked repository: git push origin feature/your_feature.
5. Create a pull request to the main repository's main branch.

Please ensure that your contributions adhere to our code of conduct and are aligned with the project's goals.

We hope this project assists you in gaining valuable insights into stock time series data and making well-informed decisions in the financial domain. Happy analyzing!

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