TSInsight: A local-global attribution framework for interpretability in time-series data
This addresses the need for interpretability in safety-critical applications using time-series data, though it is an incremental improvement over existing attribution methods.
The paper tackles the problem of interpretability in deep learning for time-series data by proposing TSInsight, a local-global attribution framework that attaches an auto-encoder to a classifier with sparsity constraints, and it was evaluated on 8 datasets against 9 other methods, showing effectiveness in output space contraction.
With the rise in the employment of deep learning methods in safety-critical scenarios, interpretability is more essential than ever before. Although many different directions regarding interpretability have been explored for visual modalities, time-series data has been neglected with only a handful of methods tested due to their poor intelligibility. We approach the problem of interpretability in a novel way by proposing TSInsight where we attach an auto-encoder to the classifier with a sparsity-inducing norm on its output and fine-tune it based on the gradients from the classifier and a reconstruction penalty. TSInsight learns to preserve features that are important for prediction by the classifier and suppresses those that are irrelevant i.e. serves as a feature attribution method to boost interpretability. In contrast to most other attribution frameworks, TSInsight is capable of generating both instance-based and model-based explanations. We evaluated TSInsight along with 9 other commonly used attribution methods on 8 different time-series datasets to validate its efficacy. Evaluation results show that TSInsight naturally achieves output space contraction, therefore, is an effective tool for the interpretability of deep time-series models.