Maximum entropy approach to multivariate time series randomization

Marcaccioli, Riccardo and Livan, Giacomo (2020) Maximum entropy approach to multivariate time series randomization. Scientific Reports, 10 (1). ISSN 2045-2322

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Abstract

Natural and social multivariate systems are commonly studied through sets of simultaneous and time-spaced measurements of the observables that drive their dynamics, i.e., through sets of time series. Typically, this is done via hypothesis testing: the statistical properties of the empirical time series are tested against those expected under a suitable null hypothesis. This is a very challenging task in complex interacting systems, where statistical stability is often poor due to lack of stationarity and ergodicity. Here, we describe an unsupervised, data-driven framework to perform hypothesis testing in such situations. This consists of a statistical mechanical approach—analogous to the configuration model for networked systems—for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series. We showcase its possible applications with a case study on financial portfolio selection.

Item Type:	Article
Additional Information:	© 2020, The Author(s).
Divisions:	Systemic Risk Centre
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HA Statistics
Date Deposited:	06 Jun 2022 16:30
Last Modified:	28 Mar 2024 08:45
URI:	http://eprints.lse.ac.uk/id/eprint/115284

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