A Review and Comparison of Changepoint Detection Techniques for Climate Data

By Jaxk Reeves, Jien Chen, Xiaolan L. Wang, Robert B. Lund, Q. Lu. 2007

679 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/7a35e7aa20b6cdc696f45689ede0794a47f057fb

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In Environmental Engineering

In Global and Planetary Change

In Health, Toxicology and Mutagenesis

Citations in the corpus

New Techniques for the Detection and Adjustment of Shifts in Daily Precipitation Data Series (2010, methodology): “Readers are referred to Reeves et al. (2007) for a comprehensive review and comparison of these maximal F tests.”
Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation (2012, background): “Retrospective change-point detection accommodates various applications that allow certain delays, for example, climate change detection (Reeves et al., 2007), genetic time-series analysis (Wang et al., 2011), signal segmentation (Basseville and Nikiforov, 1993), and intrusion detection in computer…”
Changepoint Detection in Periodic and Autocorrelated Time Series (2007, methodology): “Reeves et al. (2007) provide a recent overview of regression response function selection issues in changepoint settings.”
A Survey of Methods for Time Series Change Point Detection (2016, neutral): “Climate analysis, monitoring, and predictionmethods that utilize change point detection have become increasingly important over the last few decades due to the possible occurrence of climate change and the increase in greenhouse gases in the atmosphere [24,32,51].”
A review of novelty detection (2014, methodology): “This statistic is usually based on a likelihood ratio, but other approaches exist [149,150].”
Homogenization of Temperature Series via Pairwise Comparisons (2009, methodology): “(DeGaetano 2006; Reeves et al. 2007).”
Assessment of global warming on the island of Tenerife, Canary Islands (Spain). Trends in minimum, maximum and mean temperatures since 1944 (2012, methodology): “Despite some interesting work in this regard (Reeves et al. 2007), little has been said about the adjustments, so we chose a method that allows the homogenization with the least possible disruption of series.”
Penalized Maximal t Test for Detecting Undocumented Mean Change in Climate Data Series (2007, background): “Readers are referred to Reeves et al. (2007) for a recent, comprehensive review and comparison of these methods and to Peterson et al. (1998) for a review of homogeneity adjustments of in situ atmospheric climate data.”
Mean shift testing in correlated data (2011, background): “2011, 32 498–511 2011 Blackwell Publishing Ltd. 4 9 8 changepoint tests in climatology (Lund and Reeves, 2002; Reeves et al., 2007 and the references therein).”
A review of change point detection methods (2018, neutral): “In this work, we consider a general class of algorithms which encompasses a large part of the literature [2, 4–7, 9, 18, 22, 24, 25, 27, 33, 44, 49, 54, 58, 65–67, 70, 72, 72, 78, 81, 90, 99, 100, 117, 118].”
Benchmarking homogenization algorithms for monthly data (2012, methodology): “tailed study using multiple absolute homogenization methods (Reeves et al., 2007) would be worthwhile.”
changepoint: An R Package for Changepoint Analysis (2014, methodology): “We now introduce the general likelihood-ratio based approach to test this hypothesis.”
Penalized Maximal F Test for Detecting Undocumented Mean Shift without Trend Change (2008, background): “1 © 2008 American Meteorological Society JTECHA982 Most comprehensively, Reeves et al. (2007) review and compare eight of them, including (but not limited to) the standard normal homogeneity test (SNHT) for mean shifts in time series of zero trend (Alexandersson 1986), the common-trend two-phase…”
Accounting for Autocorrelation in Detecting Mean Shifts in Climate Data Series Using the Penalized Maximal t or F Test (2008, result): “There have also emerged several studies that review and compare different methods for testing artificial shifts (e.g., Easterling and Peterson 1995; Peterson et al. 1998; DeGaetano 2006; Reeves et al. 2007).”
Structural breaks in time series (2013, background): “…in quality control (Page, 1954, 1955) but has since become an integral part of a wide variety of fields with a significant statistical component, among them economics (Perron, 2006), finance (Andreou and Ghysels, 2009), climatology (Reeves et al., 2007) and engineering (Stoumbos et al. 2000).”