Time series foundation models (TSFMs) increasingly offer covariate support, with calendar information being the most common type of categorical covariate in practice, as it is available for every point in time. However, how these features are encoded is largely treated as an implementation detail rather than as an obje...
Alexander Kreusel, M. Beichter, Alexandra Nikoltchovska et al.· 0 citations
Modern power systems face growing operational complexity driven by the integration of renewable energy sources, decentralization, and the need for real-time decision-making across a wide range of timescales. Addressing these challenges traditionally relies on model-based methods that, while accurate, can be too slow fo...
Martin Sadric, Sebastian Pütz, Christian Nauck et al.· 0 citations
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...
Hadeer El Ashhab, Sai Srijan Papineni, M. Dorn et al.· IEEE Access· 0 citations
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