Extraction of Important Temporal Order for eXplainable AI on Time-series data

Yasutaka Nishimura, Naoto Takeda, Roberto Legaspi, Kazushi Ikeda, Thomas Plötz, Sonia Chernova

Abstract: We propose a new eXplainable AI method on time-series data in which multiple events are arranged in temporal order, to extract important temporal order between events for opaque model decision. Toward this, our proposed method analyzes the model behavior when inputting perturbated data generated by changing the order of the events. We evaluate our method via an exemplary classification task on a simulated dataset to confirm how accurate our method is in extracting important temporal order of events. In addition, we evaluate our method on a smart home sensor dataset to demonstrate what kind of important order is actually extracted.