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A hybrid approach to regime shift detection.

Alexander von Eye1, Wolfgang Wiedermann2, Stefan von Weber3

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This study introduces a novel statistical method to pinpoint regime shifts in frequency data by identifying extreme deviations. The approach aids in understanding process changes and analyzing data before and after critical shift points.

Keywords:
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Area of Science:

  • Statistics
  • Time Series Analysis
  • Data Science

Background:

  • Regime shifts represent critical changes in the dynamics of a system.
  • Analyzing these shifts is crucial for understanding complex processes.
  • Existing methods may not adequately capture the nuances of frequency data shifts.

Purpose of the Study:

  • To propose a novel statistical method for analyzing regime shifts in frequency data.
  • To identify extreme deviation points within a process's development.
  • To provide a framework for describing processes before and after shift points.

Main Methods:

  • A hybrid statistical approach combining standard model parameter estimation with Configural Frequency Analysis.
  • Estimation of functions describing the process based on a statistical model.
  • Uni- and multivariate versions of the method are developed.
  • Shift points can be predefined or estimated directly from the data.

Main Results:

  • The proposed method effectively identifies extreme deviations indicative of regime shifts.
  • The method allows for the description of data series both before and after identified shift points.
  • Application to road traffic data from California and Germany demonstrates the method's utility.

Conclusions:

  • The developed method offers a robust tool for regime shift analysis in frequency data.
  • It provides insights into process dynamics by characterizing periods around shift points.
  • Potential extensions suggest broader applicability in various scientific domains.