Davide Serramazza

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:

1. What are the current explanation methods for MTSC, and what are their strengths and limitations?
2. How do we objectively evaluate and compare multiple explanation methods for MTSC, especially for scenarios where their outputs disagree (e.g. for saliency-based methods, different explanations provide very different saliency maps)?
3. How can we integrate the insights gained from the explanation methods to improve the accuracy of the classification algorithms (eg an iterative optimisation approach)?
4. How can we integrate the insights gained from the explanation methods to improve or reduce the input data (eg, by removing noisy data, selecting good features)?