Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling
We introduce Timer-S1, a strong Mixture-of-Experts (MoE) time series foundation model with 8.3B total parameters, 0.75B activated parameters for each token, and a context length of 11.5K. To overcome the scalability bottleneck in existing pre-trained time series foundation models, we perform Serial Scaling in three dimensions: model architecture, dataset, and training pipeline. Timer-S1 integrates sparse TimeMoE blocks and generic TimeSTP blocks for Serial-Token Prediction (STP), a generic training objective that adheres to the serial nature of forecasting. The proposed paradigm introduces serial computations to improve long-term predictions while avoiding costly rolling-style inference and pronounced error accumulation in the standard next-token prediction. Pursuing a high-quality and unbiased training dataset, we curate TimeBench, a corpus with one trillion time points, and apply meticulous data augmentation to mitigate predictive bias. We further pioneer a post-training stage, including continued pre-training and long-context extension, to enhance short-term and long-context performance. Evaluated on the large-scale GIFT-Eval leaderboard, Timer-S1 achieves state-of-the-art forecasting performance, attaining the best MASE and CRPS scores as a pre-trained model. Timer-S1 will be released to facilitate further research.
69aa476ec4f6edddcc6c862a
Yong Liu
claimed_verified
2026-03-06T13:55:29.620Z
64fbe24a2d20ced4e91de38a
https://cdn-avatars.huggingface.co/v1/production/uploads/64fbe24a2d20ced4e91de38a/AR5IimFZPqLWW4Mf9QXfr.jpeg
Yong Liu
Yong99
user
Yong99
69aa476ec4f6edddcc6c862b
Xingjian Su
69aa476ec4f6edddcc6c862c
Shiyu Wang
69aa476ec4f6edddcc6c862d
Haoran Zhang
69aa476ec4f6edddcc6c862e
Haixuan Liu
69aa476ec4f6edddcc6c862f
Yuxuan Wang
69aa476ec4f6edddcc6c8630
Zhou Ye
69aa476ec4f6edddcc6c8631
Yang Xiang
69aa476ec4f6edddcc6c8632
Jianmin Wang
69aa476ec4f6edddcc6c8633
Mingsheng Long
Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling
We introduce Timer-S1, a strong Mixture-of-Experts (MoE) time series foundation model with 8.3B total parameters, 0.75B activated parameters for each token, and a context length of 11.5K. To overcome the scalability bottleneck in existing pre-trained time series foundation models, we perform Serial Scaling in three dimensions: model architecture, dataset, and training pipeline. Timer-S1 integrates sparse TimeMoE blocks and generic TimeSTP blocks for Serial-Token Prediction (STP), a generic training objective that adheres to the serial nature of forecasting. The proposed paradigm introduces serial computations to improve long-term predictions while avoiding costly rolling-style inference and pronounced error accumulation in the standard next-token prediction. Pursuing a high-quality and unbiased training dataset, we curate TimeBench, a corpus with one trillion time points, and apply meticulous data augmentation to mitigate predictive bias. We further pioneer a post-training stage, including continued pre-training and long-context extension, to enhance short-term and long-context performance. Evaluated on the large-scale GIFT-Eval leaderboard, Timer-S1 achieves state-of-the-art forecasting performance, attaining the best MASE and CRPS scores as a pre-trained model. Timer-S1 will be released to facilitate further research.
Timer-S1 is a scalable Mixture-of-Experts time series model with 8.3B parameters that uses serial scaling and novel TimeMoE blocks to improve long-term forecasting accuracy.
Mixture-of-Experts TimeMoE blocks TimeSTP blocks Serial-Token Prediction long-term predictions rolling-style inference next-token prediction GIFT-Eval leaderboard MASE CRPS