Cohesive Explanation for Time Series Prediction
Bin Li, Emmanuel Müller · 2024
Perturbation-based time series interpretation suffers under two challenges: firstly, the long and multivariate time series may lead to various incoherent salient spots on the saliency map, and secondly, common perturbation techniques often return unrealistic sequences. In this paper, we propose Cohesive Explanation for Time Series (CETS). This time series interpretation approach provides cohesive (a notion of concentrated salient features at adjacent timestamps) feature attribution using realistic prototype-based perturbations. CETS ensures a cohesive interpretation by employing both global (temporal) and local (spatial) perturbations of time series. These perturbations confine the interpretation to a concise temporal event within a specific subspace. We perform extensive experiments on real-world benchmark datasets to demonstrate the efficacy of our interpretations. Specifically, we visually illustrate how cohesive attributions contribute to enhancing the interpretability of intricate time series data. Our empirical results show that CETS achieves interpretation quality comparable to state-of-the-art approaches while providing cohesive and easy-to-understand explanations.