This project investigates methods for explaining anomalies detected in time series data. The aim is not only to identify when an anomaly occurs, but also to provide understandable explanations of why the observation or time period was considered anomalous. The project may explore feature importance, subsequence explanations, prototype examples, counterfactuals, or other explainable AI techniques for time series anomaly detection.
This project would be ideal for BA ICS/MCS, BA CS (JH), BA CSLL and integrated masters. Experience with python programming and an avid interest in machine learning is desirable. Experience with pytorch and a strong track record of projects on Github is a plus.