V. Hagenmeyer

On the Encoding of Calendar Features in Covariate-Informed Time Series Foundation Models

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...

Evaluating Time-Series Foundation Models for Cooling Demand Forecasting with Little Data

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...