Abstract
Probabilistic time series forecasting is crucial for quantifying future uncertainty, with significant applications in fields such as energy and finance. However, existing methods often rely on computationally expensive sampling or restrictive parametric assumptions to characterize future distributions, which limits predictive performance and introduces distributional mismatch. To address these challenges, this paper presents TimeGMM, a novel probabilistic forecasting framework based on Gaussian Mixture Models (GMM) that captures complex future distributions in a single forward pass. A key component is GMM-adapted Reversible Instance Normalization (GRIN), a novel module designed to dynamically adapt to temporal-probabilistic distribution shifts. The framework integrates a dedicated Temporal Encoder (TE-Module) with a Conditional Temporal-Probabilistic Decoder (CTPD-Module) to jointly capture temporal dependencies and mixture distribution parameters. Extensive experiments demonstrate that TimeGMM consistently outperforms state-of-the-art methods, achieving maximum improvements of 22.48\% in CRPS and 21.23\% in NMAE.
Abstract (translated)
概率时间序列预测对于量化未来不确定性至关重要，在能源和金融等领域有着重要应用。然而，现有的方法通常依赖于计算成本高昂的采样或限制性的参数假设来表征未来的分布，这会限制预测性能并引入分布不匹配问题。为了应对这些挑战，本文提出了TimeGMM，这是一种基于高斯混合模型（GMM）的新颖概率预测框架，在单次前向传递中捕捉复杂的未来分布情况。 TimeGMM的一个关键组件是GRIN（GMM适配的可逆实例归一化），这是一个专门设计用于动态适应时间-概率分布变化的新型模块。该框架集成了一个专用的时间编码器（TE-模块）和条件时间-概率解码器（CTPD-模块），共同捕获时间依赖性和混合分布参数。 广泛的实验表明，TimeGMM在多个基准数据集上始终优于最先进的方法，在CRPS指标上的改进最大可达22.48%，在NMAE指标上的改进最大可达21.23%。
URL
https://arxiv.org/abs/2601.12288
