On the ESD 2025 PG&E dataset, hourly-binned XGBoost models with PCA weather covariates achieved lower MAPE than transformer, LSTM, TFT, and TimeGPT models in day-ahead annual load forecasting.
Mapping properties of Fourier transforms, revisited
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abstract
The paper deals with continuous and compact mappings generated by the Fourier transform between distinguished Besov spaces $B^s_p(\mathbb{R}^n) = B^s_{p,p}(\mathbb{R}^n)$, $1\le p \le \infty$, and between Sobolev spaces $H^s_p(\mathbb{R}^n)$, $1<p< \infty$. In contrast to the paper {\em H. Triebel, Mapping properties of Fourier transforms. Z. Anal. Anwend. 41 (2022), 133--152}, based mainly on embeddings between related weighted spaces, we rely on wavelet expansions, duality and interpolation of corresponding (unweighted) spaces, and (appropriately extended) Hausdorff-Young inequalities. The degree of compactness will be measured in terms of entropy numbers and approximation numbers, now using the symbiotic relationship to weighted spaces.
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cs.LG 1years
2025 1verdicts
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IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting
On the ESD 2025 PG&E dataset, hourly-binned XGBoost models with PCA weather covariates achieved lower MAPE than transformer, LSTM, TFT, and TimeGPT models in day-ahead annual load forecasting.