B1223
Title: Automatic change-point detection in time series via deep learning
Authors: Jie Li - University of Kent (United Kingdom) [presenting]
Paul Fearnhead - Lancaster University (United Kingdom)
Piotr Fryzlewicz - London School of Economics (United Kingdom)
Tengyao Wang - University of Cambridge (United Kingdom)
Abstract: Detecting change points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient methods for detecting a change will depend on both of these features, and it can be difficult for a practitioner to develop an appropriate detection method for their application of interest. It is shown how to automatically generate new offline detection methods based on training a neural network. The approach is motivated by many existing tests for the presence of a change point being representable by a simple neural network. Thus, a neural network trained with sufficient data should perform at least as well as these methods. The theory that quantifies the error rate for such an approach and how it depends on the amount of training data is presented. Empirical results show that, even with limited training data, its performance is competitive with the standard CUSUM-based classifier for detecting a change in mean when the noise is independent and Gaussian and can substantially outperform it in the presence of auto-correlated or heavy-tailed noise. The method also shows strong results in detecting and localizing changes in activity based on accelerometer data.
