Maximum Entropy approach to multivariate time series randomization

Riccardo Marcaccioli and Giacomo Livan ()

Papers from arXiv.org

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.

Date: 2019-07, Revised 2020-06
New Economics Papers: this item is included in nep-ecm
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Published in Scientific Reports volume 10, Article number: 10656 (2020)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1907.04925

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