Change detection

This article is about statistical time series analysis. For a focus on remote sensing and geographical change, see change detection (GIS). For detection of changes to web pages, see change detection and notification.
A plot of yearly volume of the Nile river at Aswan against time, an example of time series data commonly used in change detection
Yearly volume of the Nile river at Aswan, an example of time series data commonly used in change detection. Dotted line denotes a detected change point when Old Aswan Dam was built in 1902.[1]

In statistical analysis, change detection or change point detection tries to identify times when the probability distribution of a stochastic process or time series changes. In general the problem concerns both detecting whether or not a change has occurred, or whether several changes might have occurred, and identifying the times of any such changes.

Specific applications, like step detection and edge detection, may be concerned with changes in the mean, variance, correlation, or spectral density of the process. More generally change detection also includes the detection of anomalous behavior: anomaly detection.

In offline change point detection it is assumed that a sequence of length is available and the goal is to identify whether any change point(s) occurred in the series. This is an example of post hoc analysis and is often approached using hypothesis testing methods. By contrast, online change point detection is concerned with detecting change points in an incoming data stream.

  1. ^ van den Burg, Gerrit J. J.; Williams, Christopher K. I. (May 26, 2020). "An Evaluation of Change Point Detection Algorithms". arXiv:2003.06222 [stat.ML].