Regime Shift Detection Project

This site tracks the development of the software for regime shift detection in time series known as STARS. Originally, the acronym stood for the Sequential T-test Analysis of Regime Shifts, and the software contained only one module for detecting regime shifts (or change points) in mean. Later on I added two other modules designed to detect shifts in variance and correlation, which were based on the sequential F-test, rather than the t-test. It was decided, however, to keep the original acronym, which now stands for the Sequential Three-step Analysis of Regime Shifts. This name underscores the importance of doing this analysis step by step in the right order. Since the variance module works with anomalies (deviations from mean), the first step is to detect regime shifts in mean and calculate deviations from the mean values of those regimes. Similarly, the input for the correlation module is a pair of the normalized time series, i.e., anomalies divided by standard deviation. Therefore, in step 2 it is important to detect regime shifts in variance and divide anomalies by the standard deviations for those regimes. Only after the time series are normalized, the detection of regime shifts in correlation (step 3) can be performed.

The STARS software has been used in a wide variety of applications, ranging from oceanography to climate research to economics (see Overview and references in Google Scholar). It has a number of advantages over other existing methods:

  • Automatically detects regime shifts (or change points) in mean, variance, and correlation coefficient;

  • Better performance at the end of time series, indicating a possibility of a shift in real time;

  • Can be tuned up to detect shifts on different time scales;

  • No need to calculate anomalies prior to applying the method;

  • No a priori information about shifts in the series is required;

  • It can handle time series with autocorrelation, thus separating regimes due to persistence from the true regimes with different statistics.

Sergei Rodionov, Ph.D.
Climate Logic, LLC
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