Explainable AI for Time Series via Virtual Inspection Layers

Understand

The field of eXplainable Artificial Intelligence (XAI) has greatly advanced in recent years, but progress has mainly been made in computer vision and natural language processing.

• For time series, where the input is often not interpretable, only limited research on XAI is available.
• In this work, we put forward a virtual inspection layer, that transforms the time series to an interpretable representation and allows to propagate relevance attributions to this representation via local XAI methods like layer-wise relevance propagation (LRP).
• In this way, we extend the applicability of a family of XAI methods to domains (e.g.