What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-T emporal Context? - Supplementary material Anonymous Author(s) Affiliation Address email

We use a cyclical embedding to encode these time features discarding the year. We adapted the majority of the baselines using the Time Series Library (TSlib (Wu et al., 2023)), The training of the baselines took place on a single RTX8000 GPU over the course of 100 epochs. During training, a batch size of 64 was consistently employed. Plateau, which gradually decreased the learning rate by a factor of 0.5 after a patience of 10 epochs. For the hyperparameter tuning of the baselines, we employed the Orion package (Bouthillier et al., We report the MAE and RMSE for the easy and difficult splits (presented in the main paper) along with the number of data points for each split.