I recently ran a benchmark comparing tabular and time-series foundation models on German day-ahead electricity price forecasting. The models I evaluated were: Tabular foundation models: Kumo and Nori Time-series foundation models: Chronos-2 and TimesFM-3 Traditional ML baselines: Tuned LightGBM and CatBoost The foundation models were used without training on the electricity market data. I also made sure the models had access to comparable information during evaluation. The article covers the experimental setup, how to use tabular foundation models for forecasting without data leakage, the comparison against tuned tree-based models, and the impact of additional training data on the traditional ML baselines. Full discussion and tutorial about the model here submitted by /u/nkafr
Originally posted by u/nkafr on r/ArtificialInteligence