Open AccessMethods for Starting the Detection of Undocumented Multiple Changepoints
Author(s) -
P Gerard-Marchant,
David E. Stooksbury,
Lynné Seymour
Publication year - 2008
Publication title -
journal of climate
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.315
H-Index - 287
eISSN - 1520-0442
pISSN - 0894-8755
DOI - 10.1175/2008jcli1956.1
Subject(s) - regression , streamflow , computer science , set (abstract data type) , data set , algorithm , climatology , artificial intelligence , data mining , geology , statistics , mathematics , cartography , geography , drainage basin , programming language
Four algorithms are given, as a first step toward the practical detection of undocumented multiple changepoints. These algorithms are based on the two-phase regression method of Lund and Reeves, as well as the robust method of Lazante. The result of each method is a set that contains statistically detectable changepoints; each candidate is then either independently validated as a changepoint or discarded. This is demonstrated and the methods are compared on artificial data, and then the methods are implemented on streamflow data from the Flint River in southwest Georgia. Most notably, the method based on two-phase regression was able to detect a well-known yet undocumented drop in streamflow from a local drought that no other methods have so far been able to detect.