[2603.03902] PatchDecomp: Interpretable Patch-Based Time Series Forecasting

Computer Science > Machine Learning arXiv:2603.03902 (cs) [Submitted on 4 Mar 2026] Title:PatchDecomp: Interpretable Patch-Based Time Series Forecasting Authors:Hiroki Tomioka, Genta Yoshimura View a PDF of the paper titled PatchDecomp: Interpretable Patch-Based Time Series Forecasting, by Hiroki Tomioka and 1 other authors View PDF HTML (experimental) 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. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.03902 [cs.LG...