Equal-weight mixture of synthetic generators matches or exceeds best single generator for time series foundation model pretraining and strengthens further with real data.
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, and Frank Hutter
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cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
FSA learns a mapping from feature space to autoregressive strategy space to improve zero-shot univariate time series forecasting over Transformer baselines under matched pretraining conditions.
Time series foundation models scale under a single training recipe, with forecast quality improving from 4M to 2.5B parameters and new SOTA results on BOOM, GIFT-Eval, and TIME benchmarks.
citing papers explorer
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Mix, Don't Pick: Why Synthetic Corpus Composition Matters for Time Series Foundation Model Pretraining
Equal-weight mixture of synthetic generators matches or exceeds best single generator for time series foundation model pretraining and strengthens further with real data.
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Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting
FSA learns a mapping from feature space to autoregressive strategy space to improve zero-shot univariate time series forecasting over Transformer baselines under matched pretraining conditions.
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Toto 2.0: Time Series Forecasting Enters the Scaling Era
Time series foundation models scale under a single training recipe, with forecast quality improving from 4M to 2.5B parameters and new SOTA results on BOOM, GIFT-Eval, and TIME benchmarks.