Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization
This addresses the issue of interpretability in time series classification for users needing to understand model decisions, though it is incremental as it builds on existing shapelet-based methods.
The paper tackled the problem of learning interpretable shapelets for time series classification by introducing adversarial regularization, resulting in shapelets that are more interpretable while maintaining classification performance comparable to state-of-the-art methods on standard benchmarks.
Times series classification can be successfully tackled by jointly learning a shapelet-based representation of the series in the dataset and classifying the series according to this representation. However, although the learned shapelets are discriminative, they are not always similar to pieces of a real series in the dataset. This makes it difficult to interpret the decision, i.e. difficult to analyze if there are particular behaviors in a series that triggered the decision. In this paper, we make use of a simple convolutional network to tackle the time series classification task and we introduce an adversarial regularization to constrain the model to learn more interpretable shapelets. Our classification results on all the usual time series benchmarks are comparable with the results obtained by similar state-of-the-art algorithms but our adversarially regularized method learns shapelets that are, by design, interpretable.