Regime shift detection group

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Report of the Regime Shift Detection Group

Leader and lecturer: Sergei Rodionov, Rapporteur: Abigail Mcquatters-Gollop and Violeta Velikova, Participants: Nesho Chipev, Snejana Moncheva, Galina Shtereva, Georgiy Daskalov, Vesselina Mihneva, Sonia Ouzunova, Violin Raikov, Elisaveta Peneva, Viktor Nikolsky, Laura Boicenko, Radka Mavrodieva, Svetla Bratanova, Alexandra Uzunova.

Theme:Overview of Regime Shift Detection Methods, regime shift detection software (author Sergei Rodionov), practical exercises.

Table of methods for detecting regime shifts and regime shift detection software are downloadable from .
Regime shift detection methods:
( for a complete list of methods, contactperson Sergei Rodionov).
• Methods for detecting shifts in the mean:

o Students T test
▪ Most common, robust, well established but you need to test for a regime shift by picking a year manually which violates the assumption that the change point should not be pre-assigned (ie each year should be given equal weight) and is not automatic.
o Bayesiananalysis
▪ Accounts for uncertainty of estimating change points and can be used for predictions but requires a mathematical model of the data and works best with a single change point scenario.
o Mann-Whitney U test
▪ Non parametric and easy to use but the data needs to be detrended.
o Lepage test
▪ Non parametric and similar toMann-Whitney U and Pettitt tests. Can be used to detect different shaped trends as well as regime shifts but is a single change point scenario. Not automatic and a single change point scenario.
o Wilcoxon rank sum
▪ Non parametric and similar to Mann-Whitney U test.
o Pettitt test
▪ Non parametric and similar to Mann-Whitney U test. Can be used to detectdifferent shaped trends as well as regime shifts but is a single change point scenario.
o Mann-Kendall test
▪ Also non parametric but not automatic and data must be detrended.
o Standard normal homogeneity test (SNHT)
▪ Very popular and mostly used for meterological time series. Not good when change points are close together in time, there aremore than 4 change points, or methodology of sampling or analysis has changed over time.
▪ Free package available on net: OnClim. Contains the standard normal homogeneity test.
o Regression based approach
▪ Better than SNHT when multiple change points exist but not sensitive to small shifts or shifts that occur within 10 points of one another.
oCUSUM test
▪ Cumulative deviation test. Easy to use but works with anomalies and using different base periods may affect results.
o Oerlemans method
▪ Based on comparison of an a priori described typical change with an interval of a given time series. Can be applied to any time series and results are easy to compare but a statistical significance test can’tbe constructed and requires the best fitting of a curve that represents an idealized break in the data. No longer used.
o Signal-to-Noise ratio
▪ A regime shift is defined when the signal to noise ration exceeds 1. Simple and easy to use but only works for a single change point.
o Intervention analysis
▪ Extension of ARIMA method. Allows forquantitative estimate for statistical significance of step interventions while accounting for autocorrelation in the time series but, like with t test, the time and type of intervention should be specified in advance.
o Markov chain Monte Carlo
▪ Strong basis on Bayesian approach. Must find best model to describe your data which makes method complex and difficult to use....
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