Explanation Methods for Multivariate Time Series Classification
Multivariate time series classification is an important computational task arising in applications where data is recorded over time and over multiple channels. For example a mobile phone or a smartwatch can record the acceleration and orientation of a person’s motion, and these signals are recorded as multivariate time series. One can classify this data to understand and predict human movement and various conditions such as fitness levels. Classification alone is not enough in many applications. In most applications we need to classify but also to understand what the model learns (e.g. why a prediction was given, based on what information in the data?).
The main focus of this project is on understanding, developing and evaluating explanation methods tailored to Multivariate Time Series Classification (MTSC).
Some of the main research questions are: