# Detecting regime transitions in time series using dynamic mode decomposition

## TL;DR

This paper introduces a computationally efficient method using dynamic mode decomposition and the Koopman operator to detect regime shifts and transient dynamics in time series data, demonstrated on both equations and atmospheric data.

## Contribution

The paper presents a novel approach combining Koopman operator theory and dynamic mode decomposition to identify regime transitions in time series efficiently.

## Key findings

- Successfully detected transient dynamics in the Kuramoto-Sivashinsky equation.
- Identified regime changes in atmospheric circulation around 1970.
- Provided a practical tool for analyzing complex time series transitions.

## Abstract

We employ the framework of the Koopman operator and dynamic mode decomposition to devise a computationally cheap and easily implementable method to detect transient dynamics and regime changes in time series. We argue that typically transient dynamics experiences the full state space dimension with subsequent fast relaxation towards the attractor. In equilibrium, on the other hand, the dynamics evolves on a slower time scale on a lower dimensional attractor. The reconstruction error of a dynamic mode decomposition is used to monitor the inability of the time series to resolve the fast relaxation towards the attractor as well as the effective dimension of the dynamics. We illustrate our method by detecting transient dynamics in the Kuramoto-Sivashinsky equation. We further apply our method to atmospheric reanalysis data; our diagnostics detects the transition from a predominantly negative North Atlantic Oscillation (NAO) to a predominantly positive NAO around 1970, as well as the recently found regime change in the Southern Hemisphere atmospheric circulation around 1970.