Explanations of time series models are useful for high stakes applications
like healthcare but have received little attention in machine learning
literature. We propose FIT, a framework that evaluates the importance of
observations for a multivariate time-series black-box model by quantifying the
shift in the predictive distribution over time. FIT defines the importance of
an observation based on its contribution to the distributional shift under a
KL-divergence that contrasts the predictive distribution against a
counterfactual where the rest of the features are unobserved. We also
demonstrate the need to control for time-dependent distribution shifts. We
compare with state-of-the-art baselines on simulated and real-world clinical
data and demonstrate that our approach is superior in identifying important
time points and observations throughout the time series.

我们提出了 FIT 框架来评估多元时间序列黑匣子模型中观察值的重要性，并且与现有的基线方法比较，在模拟和真实世界的临床数据上证明我们的方法在确定时间序列中的重要时间点和观察值方面更具优越性。