$t_0$: A Time-Series Foundation Model for Forecasting with Context
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at 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.
WhatIf-Bench is a semi‑synthetic benchmark that tests how well time‑series forecasters use covariates to predict a target’s response to future events. It generates 37,665 tasks from mechanistic simulators of data centers, power grids, road traffic, and water networks, varying covariate numbers, historical events, and event length. Evaluation shows large‑language models outperform the best time‑series foundation models, with all models performing well on replayed events but struggling on novel covariate settings, highlighting generalization as the main challenge.
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