AmirhosseinHonardoust/LSTM-Time-Series-Forecasting
A hands-on project for forecasting time-series with PyTorch LSTMs. It creates realistic daily data (trend, seasonality, events, noise), prepares it with sliding windows, and trains an LSTM to make multi-step predictions. The project tracks errors with RMSE, MAE, MAPE and shows clear plots of training progress and forecast results.
This project helps data scientists and analysts build and evaluate time-series forecasting models. It takes historical daily data, processes it, and produces multi-step predictions for future values. The output includes performance metrics and clear visualizations of the forecast versus actuals, alongside training progress.
No commits in the last 6 months.
Use this if you need to quickly set up, train, and visualize a robust LSTM-based time-series forecasting pipeline using your own daily data.
Not ideal if you're looking for a fully managed, production-ready forecasting service or if you need to forecast very high-frequency data beyond daily granularity.
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27
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Language
Python
License
MIT
Category
Last pushed
Sep 11, 2025
Commits (30d)
0
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