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

• URL: http://arxiv.org/abs/2511.18539v1
• Date: Sun, 23 Nov 2025 17:10:07 GMT
• Title: TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting
• Authors: Lingyu Jiang, Lingyu Xu, Peiran Li, 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,
• Abstract summary: TimePre is a novel framework that unifies the efficiency of models with the distributional flexibility of the MCL paradigm.<n>TimePre achieves new state-of-the-art accuracy on key probabilistic metrics.<n>It thus bridges the long-standing gap between accuracy, efficiency, and stability in probabilistic forecasting.
• Score: 30.11463473977148
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Probabilistic Time-Series Forecasting (PTSF) is critical for uncertainty-aware decision making, but existing generative models, such as diffusion-based approaches, are computationally prohibitive due to expensive iterative sampling. Non-sampling frameworks like Multiple Choice Learning (MCL) offer an efficient alternative, but suffer from severe training instability and hypothesis collapse, which has historically hindered their performance. This problem is dramatically exacerbated when attempting to combine them with modern, efficient MLP-based backbones. To resolve this fundamental incompatibility, we propose TimePre, a novel framework that successfully unifies the efficiency of MLP-based models with the distributional flexibility of the MCL paradigm. The core of our solution is Stabilized Instance Normalization (SIN), a novel normalization layer that explicitly remedies this incompatibility. SIN stabilizes the hybrid architecture by correcting channel-wise statistical shifts, definitively resolving the catastrophic hypothesis collapse. Extensive experiments on six benchmark datasets demonstrate that TimePre achieves new state-of-the-art accuracy on key probabilistic metrics. Critically, TimePre achieves inference speeds orders of magnitude faster than sampling-based models and, unlike prior MCL work, demonstrates stable performance scaling. It thus bridges the long-standing gap between accuracy, efficiency, and stability in probabilistic forecasting.

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