TimePre让时间序列概率预测更准更快更稳。
TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting
- 用SIN归一化层解决混合模型的统计偏移问题。
- 在6个数据集上达到当前最优概率预测精度。
- 推理速度比采样模型快数个数量级，适合实时应用。
我们提出TimePre，一种统一MLP模型高效性与多选择学习（MCL）分布灵活性的框架，用于概率时间序列预测（PTSF）。核心是稳定实例归一化（SIN），一种显式缓解准确率、效率与稳定性之间权衡的归一化层。SIN通过纠正通道级统计偏移，稳定混合架构，从而解决灾难性假设崩溃问题。在六个基准数据集上的大量实验表明，TimePre在关键概率指标上达到当前最优（SOTA）精度。关键的是，其推理速度比基于采样的模型快数个数量级，且比以往MCL方法更具稳定性。
原文摘要 · Abstract (English)
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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