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A Comparison of Single and Multiple Changepoint Techniques for Time Series Data
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In this paper, the authors compare single and multiple changepoint techniques for time series data and introduce a new distance metric specifically designed to compare two multiple-changepoint segmentation methods.Abstract:
This paper describes and compares several prominent single and multiple changepoint techniques for time series data. Due to their importance in inferential matters, changepoint research on correlated data has accelerated recently. Unfortunately, small perturbations in model assumptions can drastically alter changepoint conclusions; for example, heavy positive correlation in a time series can be misattributed to a mean shift should correlation be ignored. This paper considers both single and multiple changepoint techniques. The paper begins by examining cumulative sum (CUSUM) and likelihood ratio tests and their variants for the single changepoint problem; here, various statistics, boundary cropping scenarios, and scaling methods (e.g., scaling to an extreme value or Brownian Bridge limit) are compared. A recently developed test based on summing squared CUSUM statistics over all times is shown to have realistic Type I errors and superior detection power. The paper then turns to the multiple changepoint setting. Here, penalized likelihoods drive the discourse, with AIC, BIC, mBIC, and MDL penalties being considered. Binary and wild binary segmentation techniques are also compared. We introduce a new distance metric specifically designed to compare two multiple changepoint segmentations. Algorithmic and computational concerns are discussed and simulations are provided to support all conclusions. In the end, the multiple changepoint setting admits no clear methodological winner, performance depending on the particular scenario. Nonetheless, some practical guidance will emerge.read more
Citations
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Autocovariance estimation in the presence of changepoints
TL;DR: In this article , a Yule-Walker moment estimator for the autoregressive parameters in a dependent time series contaminated by mean shift changepoints is proposed and studied, based on first order differences of the series and proven consistent and asymptotically normal when the number of changepoints m and the series length N satisfy
Journal ArticleDOI
Changepoint Detection: An Analysis of the Central England Temperature Series
TL;DR: In this paper , a changepoint analysis is performed to detect abrupt changes, which can be regarded as a preliminary step before further analysis is conducted to identify the causes of the changes (e.g., artificial, human-induced or natural variability).
Posted Content
Autocovariance Estimation in the Presence of Changepoints
TL;DR: In this paper, a Yule-Walker moment estimator for the autoregressive parameters in a dependent time series contaminated by mean shift changepoints is proposed and studied, based on first order differences of the series and proven consistent and asymptotically normal when the number of changepoints $m$ and the series length $N$ satisfies
Journal ArticleDOI
A weighted U-statistic based change point test for multivariate time series
Journal ArticleDOI
A shape-based multiple segmentation algorithm for change-point detection
TL;DR: Wang et al. as discussed by the authors proposed a shape-based multiple segmentation algorithm, which is a generalization of binary segmentation for detecting and localization of change points for the off-line sequence of observations.
References
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Estimating and testing linear models with multiple structural changes
Jushan Bai,Pierre Perron +1 more
TL;DR: In this article, the authors developed the statistical theory for testing and estimating multiple change points in regression models, and several test statistics were proposed to determine the existence as well as the number of change points.
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A. J. Scott,M. Knott +1 more
TL;DR: In this paper, the authors used the techniques of cluster analysis to split the treatments into reasonably homogeneous groups and developed a likelihood ratio test for judging the significance of differences among the resulting groups.
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Circular binary segmentation for the analysis of array-based DNA copy number data.
TL;DR: A modification ofbinary segmentation is developed, which is called circular binary segmentation, to translate noisy intensity measurements into regions of equal copy number in DNA sequence copy number.
Journal ArticleDOI
Optimal Detection of Changepoints With a Linear Computational Cost
TL;DR: This work considers the problem of detecting multiple changepoints in large data sets and introduces a new method for finding the minimum of such cost functions and hence the optimal number and location of changepoints that has a computational cost which is linear in the number of observations.