论文标题
TimeXplain-解释时间序列分类器的预测的框架
timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers
论文作者
论文摘要
现代时间序列分类器具有令人印象深刻的预测能力，但他们的决策过程主要是用户的黑匣子。同时，只要有精心设计的域映射，就可以解释机器学习模型的模型 - 不足的解释器（例如最近提出的Shap）。我们将两个世界融合在一起，在我们的TimeXplain框架中，将可解释的人工智能的覆盖范围扩展到了时间序列分类和价值预测。我们为时域，频域和时间序列统计介绍了新颖的域映射，并分析其阐明功率及其限制。我们采用一种新颖的评估指标来实验性地将Timexplain与最新时间序列分类器的几种模型特定解释方法进行比较。
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.
