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Time series foundation models (TSFMs) refer to models pretrained on large-scale multi-domain time series data that can be transferred to downstream forecasting tasks via zero-shot or lightweight fine-tuning; representative works include Chronos, TimesFM, Moirai, and others.
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10/22/2026
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UnverifiedTime-Series Foundation Models are general-purpose forecasting models pretrained on large-scale, multi-domain time-series data with cross-task generalization capabilities, with representative works including TimesFM, Chronos, and Moirai.74% similarUnverifiedTimesFM draws on the pre-training + zero-shot inference paradigm of large language models, enabling direct forecasting of unseen time series after pre-training without targeted training.74% similarUnverifiedTimesFM 3.0 is a foundation model dedicated to time series forecasting, with zero-shot forecasting capability.74% similarUnverifiedTempoPFN uses synthetic time series to pretrain a linear RNN, achieving zero-shot time series forecasting.69% similarUnverifiedTTM 使用极小的 MLP Mixer 架构做时间序列的零样本预测68% similar
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