Maximum entropy approach to multivariate time series randomization

Riccardo Marcaccioli and Giacomo Livan

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

JEL-codes: C1 J1 (search for similar items in EconPapers)
Date: 2020-06-30
New Economics Papers: this item is included in nep-ets
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Published in Scientific Reports, 30, June, 2020, 10(1). ISSN: 2045-2322

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