TimeSliver : Symbolic-Linear Decomposition for Explainable Time Series Classification
This addresses the need for more reliable and interpretable explanations in time series analysis, particularly for applications requiring transparency, though it is incremental in advancing existing explainability approaches.
The paper tackles the problem of explaining time series classification models by introducing TimeSliver, a framework that uses symbolic-linear decomposition to assign importance scores to temporal segments, achieving an 11% improvement in attribution accuracy over other methods and predictive performance within 2% of state-of-the-art baselines.
Identifying the extent to which every temporal segment influences a model's predictions is essential for explaining model decisions and increasing transparency. While post-hoc explainable methods based on gradients and feature-based attributions have been popular, they suffer from reference state sensitivity and struggle to generalize across time-series datasets, as they treat time points independently and ignore sequential dependencies. Another perspective on explainable time-series classification is through interpretable components of the model, for instance, leveraging self-attention mechanisms to estimate temporal attribution; however, recent findings indicate that these attention weights often fail to provide faithful measures of temporal importance. In this work, we advance this perspective and present a novel explainability-driven deep learning framework, TimeSliver, which jointly utilizes raw time-series data and its symbolic abstraction to construct a representation that maintains the original temporal structure. Each element in this representation linearly encodes the contribution of each temporal segment to the final prediction, allowing us to assign a meaningful importance score to every time point. For time-series classification, TimeSliver outperforms other temporal attribution methods by 11% on 7 distinct synthetic and real-world multivariate time-series datasets. TimeSliver also achieves predictive performance within 2% of state-of-the-art baselines across 26 UEA benchmark datasets, positioning it as a strong and explainable framework for general time-series classification.