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Maximum Entropy approach to multivariate time series randomization

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  • Riccardo Marcaccioli
  • Giacomo Livan

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.

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  • Riccardo Marcaccioli & Giacomo Livan, 2019. "Maximum Entropy approach to multivariate time series randomization," Papers 1907.04925, arXiv.org, revised Jun 2020.
    • Handle: RePEc:arx:papers:1907.04925
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    References listed on IDEAS

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    1. G. Livan & S. Alfarano & E. Scalas, 2011. "The fine structure of spectral properties for random correlation matrices: an application to financial markets," Papers 1102.4076, arXiv.org.
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