{"ID":2838063,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.18539","arxiv_id":"2511.18539","title":"TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting","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.","short_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 th...","url_abs":"https://arxiv.org/abs/2511.18539","url_pdf":"https://arxiv.org/pdf/2511.18539v2","authors":"[\"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\"]","published":"2025-11-23T17:10:07Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CV\"]","methods":"[]","has_code":false}