Stock Prediction using Deep learning

Abstract: Stock market prediction has long been a subject of interest for investors, analysts, and researchers due to its potential to yield high financial returns. However, the volatile and non-linear nature of financial markets makes forecasting a challenging task accurate. This project explores the use of the ARIMA (Auto Regressive Integrated Moving Average) model for predicting stock prices, leveraging its strength in modeling time series data with trends and seasonality.The study involves the collection and preprocessing of historical stock data, followed by stationary testing, model selection using AIC/BIC criteria, and parameter tuning through grid search. The final ARIMA model is evaluated using standard performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).The results demonstrate that ARIMA can effectively capture short-term trends in stock prices, though it may be limited in handling abrupt market shifts or external factors influencing the market.Overall, this project provides insights into the applicability of statistical time series models like ARIMA in financial forecasting and highlights both their strengths and limitations in the context of stock market prediction.