Stock Price Prediction using ARIMA and Exponential Smoothing

This project combines ARIMA with Exponential Smoothing (ES) to predict stock prices. While ARIMA models the linear dependencies and trends, Exponential Smoothing is used to capture short-term fluctuations and the most recent trends. The project walks through data preprocessing, stationarity tests, and model evaluation for both ARIMA and Exponential Smoothing. The repository also provides a performance comparison between the two methods and evaluates their accuracy in predicting stock price movements.