Point-forecasting models for electricity price and load forecasting

This codebase implements a collection of point forecasting DL/ML models for our research in power market demand and price prediction.

Implemented Models

• FFNN: A standard feed-forward neural network.
• LSTM: A standard LSTM model.
• Transformer: A standard transformer model.
• Informer: An efficient transformer for time series forecasting. Zhou, Haoyi, et al. "Informer: Beyond efficient transformer for long sequence time-series forecasting." Proceedings of the AAAI conference on artificial intelligence. Vol. 35. No. 12. 2021.
• FGN: From the paper https://doi.org/10.48550/arXiv.2311.06190.
• LSTM-Attention-LSTM: From the paper X. Wen and W. Li, "Time Series Prediction Based on LSTM-Attention-LSTM Model," in IEEE Access, vol. 11, pp. 48322-48331, 2023, doi: 10.1109/ACCESS.2023.3276628.
• TimeXer: From the paper https://doi.org/10.48550/arXiv.2402.19072.
• CATS: Learned auxiliary time series; can be fit with any model. See the paper https://doi.org/10.48550/arXiv.2403.01673.