Work

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

Year: 2025

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2511.18539

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

Institutions: Tohoku University, World Precision Instruments (United States), Georgia Institute of Technology, Massachusetts Institute of Technology, Texas A&M University, University of Southern California, San Diego State University

Topic: Time Series Analysis and Forecasting

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7106676294

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