TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917430147678208 |
|---|---|
| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18539 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting 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 Machine Learning Computer Vision and Pattern Recognition 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. |
| title | TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.18539 |