Modern time series classifiers display impressive predictive capabilities, yet their decision-making processes mostly remain black boxes to the user. At the same time, model-agnostic explainers, such as the recently proposed SHAP, promise to make the predictions of machine learning models interpretable, provided there are well-designed domain mappings. We bring both worlds together in our timeXplain framework, extending the reach of explainable artificial intelligence to time series classification and value prediction. We present novel domain mappings for the time domain, frequency domain, and time series statistics and analyze their explicative power as well as their limits. We employ a novel evaluation metric to experimentally compare timeXplain to several model-specific explanation approaches for state-of-the-art time series classifiers.

翻译：现代时间序列分类器展现出令人瞩目的预测能力，但其决策过程对用户而言仍多为黑箱。与此同时，近期提出的SHAP等模型无关解释器虽能通过精心设计的域映射使机器学习模型预测具备可解释性，但相关研究尚存空白。我们在timeXplain框架中融合这两个领域，将可解释人工智能的适用范围拓展至时间序列分类与数值预测。本文提出时域、频域及时序统计量的新型域映射，系统分析其解释效力与局限性，并采用创新评估指标，将timeXplain与多种面向先进时间序列分类器的模型特化解释方法进行实验对比。