Changepoint Detection in Periodic and Autocorrelated Time Series

Robert Lund · Xiaolan L. Wang · Qi Lu · Jaxk H. Reeves · Colin Gallagher · Yang Feng

133 citations0/0 files storedJournal of Climate

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Advanced Statistical Process Monitoring Climate variability and models Statistical and numerical algorithms

Abstract

Abstract Undocumented changepoints (inhomogeneities) are ubiquitous features of climatic time series. Level shifts in time series caused by changepoints confound many inference problems and are very important data features. Tests for undocumented changepoints from models that have independent and identically distributed errors are by now well understood. However, most climate series exhibit serial autocorrelation. Monthly, daily, or hourly series may also have periodic mean structures. This article develops a test for undocumented changepoints for periodic and autocorrelated time series. Classical changepoint tests based on sums of squared errors are modified to take into account series autocorrelations and periodicities. The methods are applied in the analyses of two climate series.

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References

1955A Test for a Change in a Parameter Occurring at an Unknown Point 1977Testing a Sequence of Observations for a Shift in Location 1966Test Procedures for Possible Changes in Parameters of Statistical Distributions Occurring at Unknown Time Points

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2010An MDL approach to the climate segmentation problem 2022A comparison of single and multiple changepoint techniques for time series data 2012Changepoint detection in daily precipitation data 2015A General Regression Changepoint Test for Time Series Data 2015Regime change analysis of interval-valued time series with an application to PM10 2009Change detection in linear regression with time series errors 2017CHANGE POINT ANALYSIS: A STATISTICAL APPROACH TO DETECT POTENTIAL ABRUPT CHANGE 2017Detection of a changepoint, a mean-shift accompanied with a trend change, in short time-series with autocorrelation

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