Evaluating Time-Series Foundation Models for Cooling Demand Forecasting with Little Data
Alexander Kreusel, Matthias Hertel, Moritz Noskiewicz et al.
· 1 citation
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Deep Learning for Cross-Border Electricity Price Forecasting: A Comparative Study
A comparative evaluation of six deep learning models--covering state-space, MLP, RNN, and Transformer-based architectures--emphasizing generalization across markets suggests that N-HiTS and NBEATSx perform competitively in limited-data scenarios, while transformer-based models can reach comparable accuracy but tend to require more adaptation and tuning.
H.S.M. Elashhab, Sai Srijan Papineni, M. Dorn et al.
· IEEE Access · 0 citations