Comparing Deep Learning and Statistical Models for Stock Price Prediction

Link: GitHub Repository

This project explores stock price prediction using time series analysis, comparing the performance of Long Short-Term Memory (LSTM), a deep learning model, with AutoRegressive Integrated Moving Average (ARIMA), a statistical model. The primary focus is on forecasting HP Inc.’s future stock prices based on historical data. The objective is to highlight the strengths and weaknesses of both approaches in predicting stock trends.