将时间序列分块分析，既准又看得懂预测依据。
PatchDecomp: Interpretable Patch-Based Time Series Forecasting
- 把时间序列切分成小片段，逐片分析贡献度。
- 在多个数据集上表现接近顶尖模型，准确率不降。
- 能可视化每段对结果的影响，适合需要解释的场景。
时间序列预测在诸多领域中具有核心作用，推动了高精度神经网络模型的发展。然而，这些模型的复杂性常阻碍人类理解其预测背后的逻辑。我们提出 PatchDecomp，一种基于神经网络的时间序列预测方法，兼具高精度与可解释性。该方法将输入时间序列划分为子序列（片段），通过聚合各片段的贡献来生成预测，从而实现对每个片段（包括外生变量片段）的清晰归因。在多个基准数据集上的实验表明，PatchDecomp 的预测性能可与近期先进方法相媲美。此外，我们验证了模型的解释不仅在数值上影响预测结果，还能通过片段级贡献可视化提供定性可解释性。
原文摘要 · Abstract (English)
Time series forecasting, which predicts future values from past observations, plays a central role in many domains and has driven the development of highly accurate neural network models. However, the complexity of these models often limits human understanding of the rationale behind their predictions. We propose PatchDecomp, a neural network-based time series forecasting method that achieves both high accuracy and interpretability. PatchDecomp divides input time series into subsequences (patches) and generates predictions by aggregating the contributions of each patch. This enables clear attribution of each patch, including those from exogenous variables, to the final prediction. Experiments on multiple benchmark datasets demonstrate that PatchDecomp provides predictive performance comparable to recent forecasting methods. Furthermore, we show that the model's explanations not only influence predicted values quantitatively but also offer qualitative interpretability through visualization of patch-wise contributions.
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