PatchDecomp: Interpretable Patch-Based Time Series Forecasting

TL;DR

PatchDecomp is proposed, a neural network-based time series forecasting method that achieves both high accuracy and interpretability and shows that the model's explanations not only influence predicted values quantitatively but also offer qualitative interpretability through visualization of patch-wise contributions.

Abstract

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.

Figures (12)

• Figure 1: TSF framework and explanations from PatchDecomp. PatchDecomp performs predictions by dividing input time series into patches, providing users with explanations as the input patch importance (the intensity of red in the lower left figure) and the contributions of input patches to the prediction (the area charts in the lower right). For clarity, only the patches with high importance are depicted in red for each variable; others are in blue.
• Figure 2: Architecture of PatchDecomp
• Figure 3: Critical difference diagrams. The horizontal axis corresponds to the performance rankings of each method, with methods positioned further to the right on the graph indicating a relatively higher performance. The black crossbars connect methods that do not exhibit statistically significant differences.
• Figure 4: Decomposition of the contributions of the input patches. Each row represents a variable, with the colors corresponding to the input patches. The right column displays the area charts of the prediction contributions for each variable, where the final prediction is obtained by summing the contributions of all the variables.
• Figure 5: Local explanation. It represents the prediction contributions for each input patch at a specific time by the intensity of the color.