ProtoTS通过分层原型捕捉时间序列的全局与局部模式，实现高精度可解释预测。
ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting
- 用去噪表示计算实例与原型相似性，保留异构信息
- 分层原型结构在多个数据集上提升预测准确率
- 支持专家干预，提供多层级可解释性，适合医疗金融等高风险场景
深度学习在时间序列预测中表现优异，但在高风险场景下理解其决策过程愈发重要。现有可解释模型多仅提供局部、部分解释，难以揭示异构输入变量如何共同影响预测曲线的整体时序模式。本文提出ProtoTS，一种新型可解释预测框架，通过建模原型化时间模式实现高精度与透明决策。ProtoTS基于去噪表示计算实例-原型相似性，保留丰富的异构信息。原型分层组织：粗粒度原型捕捉全局时序模式，细粒度原型捕获局部变化，支持专家引导和多层级解释。在多个真实基准数据集（包括新发布的LOF数据集）上的实验表明，ProtoTS不仅在预测精度上超越现有方法，还提供可专家干预的解释，增强模型理解与决策支持。
原文摘要 · Abstract (English)
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 realistic benchmarks, including a newly released LOF dataset, show that ProtoTS not only exceeds existing methods in forecast accuracy but also delivers expert-steerable interpretations for better model understanding and decision support.
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