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arXiv Machine Learning By Alexander Kreusel, Maximilian Beichter, Alexandra Nikoltchovska, Benjamin Sch\"afer, Ralf Mikut, Frederik Zahn, Veit Hagenmeyer

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

Read the original on arXiv Machine Learning →

The paper investigates how different calendar feature encodings influence the performance of covariate-informed time series foundation models (TSFMs). Seven encoding schemes—three target‑agnostic (ordinal, one‑hot, cyclic) and four target‑based—are systematically compared across four state‑of‑the‑art TSFMs on nine datasets that reflect social activity patterns such as weekends and holidays. Results show that encoding choice can change forecast accuracy by up to 9.9 % on non‑holidays and 83 % on holidays, with improvements mainly around holidays and varying effects across models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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