Concept Extraction for Time Series With ECLAD

Abstract

Concept Extraction (CE) methods are being increasingly used in the image domain for explaining deep learning models, which are not inherently interpretable. However, there have not been transfer studies yet for their usage in the time series domain. The purpose of this work is to explore the use of CE methods in time series. We propose to modify the ECLAD algorithm for this domain by changing the latent space representation used to extract concepts. This method is then tested on an InceptionTime model trained on the Gunpoint dataset. Preliminary results show that we can successfully extract concepts from time series models on datasets with local features and provide conceptual explanations that effectively explain how the model works.