用自适应高斯混合模型实现单次前向传播的高效概率预测
TimeGMM: Single-Pass Probabilistic Forecasting via Adaptive Gaussian Mixture Models with Reversible Normalization
- 基于自适应高斯混合模型与可逆归一化，单次前向计算捕捉复杂分布
- 在CRPS和NMAE上分别提升22.48%和21.23%，超越现有方法
- 适合需要快速、精准概率预测的能源与金融场景
概率时间序列预测对量化未来不确定性至关重要，广泛应用于能源与金融领域。然而，现有方法常依赖计算昂贵的采样或受限的参数假设，导致性能受限并引入分布偏差。本文提出TimeGMM，一种基于高斯混合模型（GMM）的新颖概率预测框架，可在单次前向传播中捕捉复杂未来分布。核心组件为适配GMM的可逆实例归一化（GRIN），可动态适应时序-概率分布变化。框架融合专用时序编码器（TE-Module）与条件时序-概率解码器（CTPD-Module），联合建模时序依赖与混合分布参数。大量实验表明，TimeGMM持续优于当前最优方法，在CRPS上最高提升22.48%，在NMAE上最高提升21.23%。
原文摘要 · Abstract (English)
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.
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