A general framework to increase the robustness of model-based change point detection algorithms to outliers and noise

The autonomous identification of time-steps where the behavior of a time-series significantly deviates from a predefined model, or time-series change point detection, is an active field of research with notable applications in finance, health, and advertising. One family of time-series change detection algorithms, referred to as "model-based methods", although useful for many applications, performs poor when the data are noisy and have outliers. We introduce a new framework that enables existing model-based methods to be more robust to these data challenges. We demonstrate the effectiveness of our approach on remote sensing and mobile health data. Our method introduces two new concepts: (i) a random sampling procedure allows us to overcome outliers, and (ii) a matrix-based representation of anomaly scores provides a flexible and intuitive way to identify multiple types of changes and test their significance. We show that our method performs better than several baseline methods, including application-specific algorithms, and provide all data and open-source code.