Parameter inference from a non-stationary unknown process

Kieran S Owens; Ben D Fulcher

doi:10.1063/5.0228236

Parameter inference from a non-stationary unknown process

Chaos. 2024 Oct 1;34(10):101501. doi: 10.1063/5.0228236.

Authors

Kieran S Owens^{1

2}, Ben D Fulcher^{1

2}

Affiliations

¹ School of Physics, The University of Sydney, Camperdown, NSW 2006, Australia.
² Centre for Complex Systems, The University of Sydney, Camperdown, NSW 2006, Australia.

PMID: 39476014
DOI: 10.1063/5.0228236

Abstract

Non-stationary systems are found throughout the world, from climate patterns under the influence of variation in carbon dioxide concentration to brain dynamics driven by ascending neuromodulation. Accordingly, there is a need for methods to analyze non-stationary processes, and yet, most time-series analysis methods that are used in practice on important problems across science and industry make the simplifying assumption of stationarity. One important problem in the analysis of non-stationary systems is the problem class that we refer to as parameter inference from a non-stationary unknown process (PINUP). Given an observed time series, this involves inferring the parameters that drive non-stationarity of the time series, without requiring knowledge or inference of a mathematical model of the underlying system. Here, we review and unify a diverse literature of algorithms for PINUP. We formulate the problem and categorize the various algorithmic contributions into those based on (1) dimension reduction, (2) statistical time-series features, (3) prediction error, (4) phase-space partitioning, (5) recurrence plots, and (6) Bayesian inference. This synthesis will allow researchers to identify gaps in the literature and will enable systematic comparisons of different methods. We also demonstrate that the most common systems that existing methods are tested on-notably, the non-stationary Lorenz process and logistic map-are surprisingly easy to perform well on using simple statistical features like windowed mean and variance, undermining the practice of using good performance on these systems as evidence of algorithmic performance. We then identify more challenging problems that many existing methods perform poorly on and which can be used to drive methodological advances in the field. Our results unify disjoint scientific contributions to analyzing the non-stationary systems and suggest new directions for progress on the PINUP problem and the broader study of non-stationary phenomena.