  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.
