Chan-dre-yi/POWER-CAST
This project is a time series forecasting model using the Temporal Fusion Transformer (TFT) deep learning architecture. The model is trained and evaluated on the M4 competition dataset, achieving state-of-the-art results in multi-step forecasting tasks.
This project helps operations managers and energy analysts predict future energy demand for utilities and grids. You provide historical energy consumption data, and it outputs multi-step forecasts showing expected demand. It's designed for professionals who need accurate predictions to manage resources and plan operations.
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Use this if you need highly accurate, multi-step predictions for time-sensitive data like energy consumption, especially when long-term patterns and seasonal variations are important.
Not ideal if you're dealing with very short, simple time series or if you prefer simpler, more traditional statistical forecasting methods.
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Last pushed
Jul 17, 2025
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