Stock Price Forecasting using ARIMA

This project demonstrates how to use the ARIMA model to forecast stock prices. ARIMA (AutoRegressive Integrated Moving Average) is a classical time series forecasting method that models the data based on its past values, trends, and seasonality. The project walks through the steps of preprocessing stock market data, including stationarity tests, parameter tuning, and model diagnostics. The ARIMA model is fitted to the data, and predictions are made for future stock prices. The repository also compares the ARIMA model’s performance against other models, such as LSTM, for accuracy and robustness.