Interpretable time series prediction is crucial for safety-critical areas such as healthcare and autonomous driving. Most existing methods focus on interpreting predictions by assigning important scores to segments of time series. In this paper, we take a different and more challenging route and aim at developing a self-interpretable model, dubbed Counterfactual Time Series (CounTS), which generates counterfactual and actionable explanations for time series predictions. Specifically, we formalize the problem of time series counterfactual explanations, establish associated evaluation protocols, and propose a variational Bayesian deep learning model equipped with counterfactual inference capability of time series abduction, action, and prediction. Compared with state-of-the-art baselines, our self-interpretable model can generate better counterfactual explanations while maintaining comparable prediction accuracy.

翻译：可解释的时间序列预测对于医疗保健和自动驾驶等安全关键领域至关重要。现有方法大多通过为时间序列片段分配重要性分数来解释预测结果。本文另辟蹊径，旨在开发一种名为反事实时间序列（Counterfactual Time Series, CounTS）的可自解释模型，该模型能够为时间序列预测生成反事实且可操作的因果解释。具体而言，我们形式化定义了时间序列反事实解释问题，建立了相应的评估协议，并提出了一种具备时间序列外推、行动与预测反事实推理能力的变分贝叶斯深度学习模型。与现有最优基准方法相比，我们的可自解释模型在保持相当预测精度的同时，能够生成更优的反事实解释。