Explaining Time Series by Counterfactuals

Sana Tonekaboni, Shalmali Joshi, David Duvenaud, Anna Goldenberg

TL;DR: Explaining Multivariate Time Series Models by finding important observations in time using Counterfactuals

Abstract: We propose a method to automatically compute the importance of features at every observation in time series, by simulating counterfactual trajectories given previous observations. We define the importance of each observation as the change in the model output caused by replacing the observation with a generated one. Our method can be applied to arbitrarily complex time series models. We compare the generated feature importance to existing methods like sensitivity analyses, feature occlusion, and other explanation baselines to show that our approach generates more precise explanations and is less sensitive to noise in the input signals.

Keywords: explainability, counterfactual modeling, time series