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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title On the equivalence between the kinetic Ising model and discrete autoregressive processes
Organization Unit
Authors
  • Carlo Campajola
  • Fabrizio Lillo
  • Piero Mazzarisi
  • Daniele Tantari
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title Journal of Statistical Mechanics: Theory and Experiment
Publisher IOP Publishing
Geographical Reach international
ISSN 1742-5468
Volume 2021
Number 3
Page Range 033412
Date 2021
Abstract Text Binary random variables are the building blocks used to describe a large variety of systems, from magnetic spins to financial time series and neuron activity. In statistical physics the kinetic Ising model has been introduced to describe the dynamics of the magnetic moments of a spin lattice, while in time series analysis discrete autoregressive processes have been designed to capture the multivariate dependence structure across binary time series. In this article we provide a rigorous proof of the equivalence between the two models in the range of a unique and invertible map unambiguously linking one model parameters set to the other. Our result finds further justification acknowledging that both models provide maximum entropy distributions of binary time series with given means, auto-correlations, and lagged cross-correlations of order one. We further show that the equivalence between the two models permits to exploit the inference methods originally developed for one model in the inference of the other.
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Digital Object Identifier 10.1088/1742-5468/abe946
Other Identification Number merlin-id:21982
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