PatchDecomp: Interpretable Patch-Based Time Series Forecasting
This work addresses the need for interpretable time series forecasting for domains that rely on understanding the rationale behind predictions, such as finance or resource planning.
The authors tackled the problem of time series forecasting with a focus on interpretability, achieving comparable predictive performance to recent methods. PatchDecomp provides clear attribution of each patch to the final prediction, with experiments on multiple benchmark datasets demonstrating its effectiveness.
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.