{"repo":"AmirhosseinHonardoust/Stock-LSTM-Forecasting","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Stock-LSTM-Forecasting","clone":"git clone https://github.com/AmirhosseinHonardoust/Stock-LSTM-Forecasting.git","description":"Predict stock prices using LSTM networks in PyTorch. This project covers data preprocessing, sliding window creation, model training with early stopping, and evaluation with RMSE/MAE/MAPE. Includes visualizations of training loss, predicted vs actual prices, and short-horizon forecasts.","language":"Python","stars":38,"topics":["data-science","deep-learning","finance","forecasting","lstm","machine-learning","neural-networks","portfolio-project","predictive-modeling","python"],"license":"MIT","category":"machine-learning","readme_excerpt":"Stock Price Prediction with LSTM Stock Price Prediction with LSTM is a hands-on deep learning project that demonstrates how sequential models can be applied to real-world financial data. Using historical OHLCV (Open, High, Low, Close, Volume) data, the project builds and trains an LSTM network to capture time-dependent patterns in stock movements. The pipeline handles everything from preprocessing and sliding-window dataset creation to model training with early stopping and evaluation. The results are presented with intuitive visualizations — training and validation loss curves, predicted vs. actual stock prices, and short-horizon forecasts into the future. Metrics such as RMSE, MAE, and MAPE provide quantitative insight into performance. This project serves as both a learning tool and a portfolio-ready showcase of time-series forecasting, deep learning, and financial modeling with PyTorch. --- Features - Load stock data from CSV or fetch with Yahoo Finance (via yfinance ) - Preprocessing: scaling & sliding window dataset creation - LSTM model with dropout and Adam optimizer - Metrics: RMSE, MAE, MAPE - Plots: - Training & validation curves - Predicted vs actual prices - Short-horizon future forecast - Saved artifacts: best lstm.pt , scaler.pkl , metrics.json --- Project Structure --- Setup --- Fetch Data (optional) Or use the included synthetic dataset ( data/aapl.csv ). --- Train the Model --- Evaluate the Model --- Results Training & Validation Loss --- Predicted vs Actual","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Stock-LSTM-Forecasting/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}