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arXiv · 2506.11029

Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Model

Xue Wang·Tian Zhou·Jinyang Gao·Bolin Ding·Jingren Zhou
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Abstract

We present a joint forecasting framework for time series prediction that contrasts with traditional direct or recursive methods. This framework achieves state-of-the-art performance for our designed foundation model, YingLong, and reveals a novel scaling effect: longer outputs significantly enhance model accuracy due to delayed chain-of-thought reasoning in our non-causal approach. YingLong is a non-causal, bidirectional attention encoder-only transformer trained through masked token recovery, aligning more effectively with language understanding tasks than with generation tasks. Additionally, we boost performance by tackling output variance with a multi-input ensemble. We release four foundation models ranging from 6M to 300M parameters, demonstrating superior results in zero-shot tasks on the ETT and Weather datasets. YingLong achieves more than 60% best performance. To ensure generalizability, we assessed the models using the GIFT-Eval benchmark, which comprises 23 time series datasets across 7 domains. Yinglong significantly outperformed the best time-series foundation models, end-to-end trained models by 14% and 44% in rank respectively.The pretrained 300M model is available at https://huggingface.co/qcw1314/YingLong_300m

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Xue Wang, Tian Zhou, Jinyang Gao, Bolin Ding, Jingren Zhou. 2025-05-20. Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Model. https://arxiv.org/abs/2506.11029

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