1. REPORT of the REGIME SHIFTS DETECTION GROUP

2. Important information
Table of methods for detecting regime shifts and regime shift detection software
downloadable from

3. Methods for detecting shifts in the mean
Student’s t-test
Bayesian analysis
Mann–Whitney U-test
Wilcoxon rank sum
Pettitt test
Mann-Kendall test
Lepage test
Standard normal homogeneity test
Regression-based approach
CUSUM test
Oerlemans method
Signal-to-noise ratio
Intervention analysis
Markov chain Monte Carlo
Lanzante method

4. Shifts in the Variance Shifts in the Spectrum
Downton-Katz test
Shifts in the Spectrum
Nikiforov method

5. Shifts in the System Principal component analysis
Average standard deviates
Fisher information
Vector autoregressive method

6. Software demonstration of Shifts in mesozooplankton
community off the Bulgarian
coast

7. Aurelia aurita biomass in NW Region

8. MANY QUESTIONS

9. What kind of data we can use?
Better use annual averages. If you have monthly data, you can work with the monthly anomalies.
Software assumes there is no missing data – this problem is for the user to deal with (for example by replacing missing values with the mean).
What about samples of different sizes?
It is up to the user how to weight these samples; it is the user’s task to prepare the time series for analysis.

10. If you have a steep trend?
The software might fail to show a good result. You could perform the analyses for the time derivative of the variable. Or you can detrend the time series, it is according to your preferences.

11. Definitions of regime shifts
A regime shift occurs when a statistically significant difference exists between the mean value of the variable before and after a certain point based on the t-test.
Shifts can sometimes be ‘seen’ in red noise when there actually is no regime shift.
Xt = Xt-1 + Et (red noise)

12. See Nature paper in Vol 435/19 May 2005:
“Distinguishing random environmental fluctuations from ecological catastrophes for the North Pacific Ocean.” Chih-hao Hsieh, Sarah M. Glaser, Andrew J. Lucas and George Sugihara, Scripps Institution of Oceanography
This paper states that biological time series have actual regime shifts being non-linear while climatic don’t, at least not according to the tested data from the North Pacific Ocean, showing the hallmarks of linear stochastic generating mechanisms.

13. The regime shifts appear as a quasi-stationary states in measured parameters, separated by periods of rapid transition.
True regime shifts are not random features of the time series, but are formally associated with the ideas of nonlinear amplification, alternative basins of attraction, multiple stable states, hysteresis and fold catastrophe, all of which require the underlying dynamics to be nonlinear in origin.

14. How to be sure that we have a regime shift?
The only way to tell if there actually is a regime shift is to either model the whole system or get more data.

15. What technique can we use to find different states in a system and the underlying mechanism of change?
Cluster analysis, tree structure techniques, fuzzy stat techniques but these only give you the number of states and don’t tell anything about the dynamics between states. There is no one technique that is good for all systems.

16. THANK YOU FOR YOUR ATTENTION