Detecting Multiple Change Points Using Adaptive Regression Splines with Application to Neural Recordings

TL;DR

This paper introduces a new method called Paired Adaptive Regressors for Cumulative Sum to detect multiple change points in multivariate time series, demonstrated on neural recordings and outperforming existing methods.

Contribution

The paper presents a novel change point detection method that handles high-dimensional data efficiently and incorporates features from advanced techniques.

Findings

Effective detection of multiple change points demonstrated in simulations.

Application to neural data reveals meaningful change points during learning.

Method shows advantages over existing approaches in accuracy and flexibility.

Abstract

Time series, as frequently the case in neuroscience, are rarely stationary, but often exhibit abrupt changes due to attractor transitions or bifurcations in the dynamical systems producing them. A plethora of methods for detecting such change points in time series statistics have been developed over the years, in addition to test criteria to evaluate their significance. Issues to consider when developing change point analysis methods include computational demands, difficulties arising from either limited amount of data or a large number of covariates, and arriving at statistical tests with sufficient power to detect as many changes as contained in potentially high-dimensional time series. Here, a general method called Paired Adaptive Regressors for Cumulative Sum is developed for detecting multiple change points in the mean of multivariate time series. The method's advantages over alternative approaches are demonstrated through a series of simulation experiments. This is followed by a real data application to neural recordings from rat medial prefrontal cortex during learning. Finally, the method's flexibility to incorporate useful features from state-of-the-art change point detection techniques is discussed, along with potential drawbacks and suggestions to remedy them.