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arXiv · 2511.18539

TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting

Lingyu Jiang·Lingyu Xu·Peiran Li·Dengzhe Hou·Qianwen Ge·Dingyi Zhuang·Shuo Xing·Wenjing Chen·Xiangbo Gao·Ting-Hsuan Chen·Xueying Zhan·Xin Zhang·Ziming Zhang·Zhengzhong Tu·Michael Zielewski·Kazunori Yamada·Fangzhou Lin
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Abstract

We propose TimePre, a simple framework that unifies the efficiency of Multilayer Perceptron (MLP)-based models with the distributional flexibility of Multiple Choice Learning (MCL) for Probabilistic Time-Series Forecasting (PTSF). Stabilized Instance Normalization (SIN), the core of TimePre, is a normalization layer that explicitly addresses the trade-off among accuracy, efficiency, and stability. SIN stabilizes the hybrid architecture by correcting channel-wise statistical shifts, thereby resolving the catastrophic hypothesis collapse. Extensive experiments on six benchmark datasets demonstrate that TimePre achieves state-of-the-art (SOTA) accuracy on key probabilistic metrics. Critically, TimePre achieves inference speeds that are orders of magnitude faster than sampling-based models, and is more stable than prior MCL approaches.

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Lingyu Jiang, Lingyu Xu, Peiran Li, Dengzhe Hou, Qianwen Ge, Dingyi Zhuang, Shuo Xing, Wenjing Chen, Xiangbo Gao, Ting-Hsuan Chen, Xueying Zhan, Xin Zhang, Ziming Zhang, Zhengzhong Tu, Michael Zielewski, Kazunori Yamada, Fangzhou Lin. 2025-11-23. TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting. https://arxiv.org/abs/2511.18539

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