Phase Walk Analysis of Leptokurtic Time Series

TL;DR

This paper introduces phase walk analysis, a novel method for detecting nonlinearities in time series by analyzing Fourier phase correlations, demonstrated on leptokurtic noise and real-world data, with improved computational efficiency.

Contribution

The paper presents a new phase walk analysis technique that detects nonlinearities in time series through Fourier phase correlations, offering faster computation and more precise significance testing.

Findings

Effective detection of nonlinearities in leptokurtic and real-world time series.

Significantly reduced computation time compared to embedding methods.

Enhanced accuracy in significance testing with increased surrogate realizations.

Abstract

The Fourier phase information play a key role for the quantified description of nonlinear data. We present a novel tool for time series analysis that identifies nonlinearities by sensitively detecting correlations among the Fourier phases. The method, being called phase walk analysis, is based on well established measures from random walk analysis, which are now applied to the unwrapped Fourier phases of time series. We provide an analytical description of its functionality and demonstrate its capabilities on systematically controlled leptokurtic noise. Hereby, we investigate the properties of leptokurtic time series and their influence on the Fourier phases of time series. The phase walk analysis is applied to measured and simulated intermittent time series, whose probability density distribution are approximated by power laws. We use the day-to-day returns of the Dow-Jones industrial average, a synthetic time series with tailored nonlinearities mimicing the power law behavior of the Dow-Jones and the acceleration of wind at an Atlantic offshore site. Testing for nonlinearities by means of surrogates shows that the new method yields strong significances for nonlinear behavior. Due to the drastically decreased computing time as compared to embedding space methods the number of surrogate realizations can be increased by orders of magnitude. Thereby, the probability distribution of the test statistics can very accurately be derived and parametrized which allows for much more precise tests on nonlinearities.