[2509.23159] ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting

Computer Science > Machine Learning arXiv:2509.23159 (cs) [Submitted on 27 Sep 2025 (v1), last revised 27 Feb 2026 (this version, v4)] Title:ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting Authors:Ziheng Peng, Shijie Ren, Xinyue Gu, Linxiao Yang, Xiting Wang, Liang Sun View a PDF of the paper titled ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting, by Ziheng Peng and 5 other authors View PDF HTML (experimental) Abstract:While deep learning has achieved impressive performance in time series forecasting, it becomes increasingly crucial to understand its decision-making process for building trust in high-stakes scenarios. Existing interpretable models often provide only local and partial explanations, lacking the capability to reveal how heterogeneous and interacting input variables jointly shape the overall temporal patterns in the forecast curve. We propose ProtoTS, a novel interpretable forecasting framework that achieves both high accuracy and transparent decision-making through modeling prototypical temporal patterns. ProtoTS computes instance-prototype similarity based on a denoised representation that preserves abundant heterogeneous information. The prototypes are organized hierarchically to capture global temporal patterns with coarse prototypes while capturing finer-grained local variations with detailed prototypes, enabling expert steering and multi-level interpretability. Experiments on multiple re...