论文标题
时间序列数据的特征重要性：改进内核变形
Feature Importance for Time Series Data: Improving KernelSHAP
论文作者
论文摘要
特征重要的技术在可解释的AI文献中广泛关注，以确定训练有素的机器学习模型如何做出预测。我们考虑在时间序列数据的上下文中应用的基于Shapley价值的方法以具有重要性。我们为包括Varmax在内的许多时间序列模型的形状值提供了封闭的形式解决方案。我们还展示了如何将内核变形应用于时间序列任务，以及如何将此技术的特征重要性组合起来以执行“事件检测”。最后，我们探讨了时间一致的shapley值的使用，以提高特征的重要性。
Feature importance techniques have enjoyed widespread attention in the explainable AI literature as a means of determining how trained machine learning models make their predictions. We consider Shapley value based approaches to feature importance, applied in the context of time series data. We present closed form solutions for the SHAP values of a number of time series models, including VARMAX. We also show how KernelSHAP can be applied to time series tasks, and how the feature importances that come from this technique can be combined to perform "event detection". Finally, we explore the use of Time Consistent Shapley values for feature importance.
