A trustworthy machine learning model should be accurate as well as explainable. Understanding why a model makes a certain decision defines the notion of explainability. While various flavors of explainability have been well-studied in supervised learning paradigms like classification and regression, literature on explainability for time series forecasting is relatively scarce. In this paper, we propose a feature-based explainability algorithm, TsSHAP, that can explain the forecast of any black-box forecasting model. The method is agnostic of the forecasting model and can provide explanations for a forecast in terms of interpretable features defined by the user a prior. The explanations are in terms of the SHAP values obtained by applying the TreeSHAP algorithm on a surrogate model that learns a mapping between the interpretable feature space and the forecast of the black-box model. Moreover, we formalize the notion of local, semi-local, and global explanations in the context of time series forecasting, which can be useful in several scenarios. We validate the efficacy and robustness of TsSHAP through extensive experiments on multiple datasets.

翻译：一个可信的机器学习模型不仅应具备高精度，还应具备可解释性。理解模型为何做出特定决策定义了可解释性的核心概念。尽管在分类和回归等监督学习范式中，各类可解释性方法已得到充分研究，但针对时间序列预测的可解释性文献相对匮乏。本文提出基于特征的可解释性算法TsSHAP，可解释任意黑箱预测模型的预测结果。该方法与预测模型无关，能基于用户预先定义的可解释特征为预测提供解释。解释结果以SHAP值形式呈现，这些SHAP值通过应用TreeSHAP算法训练代理模型获得——该代理模型学习可解释特征空间与黑箱模型预测结果之间的映射关系。此外，我们系统定义了时间序列预测场景下的局部、半局部和全局解释概念，这些概念在多种应用场景中具有实用价值。通过在多个数据集上的广泛实验，我们验证了TsSHAP的有效性与鲁棒性。