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Time series analysis and prediction of Amazon stock prices using LSTM. Built with PyTorch, this project leverages deep learning for forecasting trends, featuring data preprocessing, model training, and evaluation.

A comprehensive stock prediction system combining ARIMA time series analysis for long-term forecasting (2027) and LSTM neural networks for short-term predictions. Features data preprocessing with MinMaxScaler, model training with PyTorch, and visualization of actual vs predicted prices with residual analysis.

A deep learning project for forecasting Apple stock closing prices using historical multi-company stock data. This repo compares transformer-based models (BERT, RoBERTa) with an LSTM-based model for multi-step time series prediction. Implemented in PyTorch with recursive forecasting and performance evaluation across multiple lags.

PyTorch-based stock price prediction pipeline that downloads historical market data, scales features, builds and trains an LSTM neural network, evaluates performance with RMSE, and visualizes actual vs predicted prices to model time-series trends and forecast future closing prices.