Information Geometric Theory in the Prediction of Abrupt Changes in System Dynamics

A.-J. Guel-Cortez, Eun-jin Kim

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)
66 Downloads (Pure)


Detection and measurement of abrupt changes in a process can provide us with important tools for decision making in systems management. In particular, it can be utilised to predict the onset of a sudden event such as a rare, extreme event which causes the abrupt dynamical change in the system. Here, we investigate the prediction capability of information theory by focusing on how sensitive information-geometric theory (information length diagnostics) and entropy-based information theoretical method (information flow) are to abrupt changes. To this end, we utilise a non-autonomous Kramer equation by including a sudden perturbation to the system to mimic the onset of a sudden event and calculate time-dependent probability density functions (PDFs) and various statistical quantities with the help of numerical simulations. We show that information length diagnostics predict the onset of a sudden event better than the information flow. Furthermore, it is explicitly shown that the information flow like any other entropy-based measures has limitations in measuring perturbations which do not affect entropy.
Original languageEnglish
Article number694
Number of pages24
Issue number6
Publication statusPublished - 31 May 2021

Bibliographical note

This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license (https://


Leverhulme Trust Research Fellowship (RF- 2018-142-9)


  • Abrupt events
  • Entropy
  • Information flow
  • Information geometry
  • Information length
  • Prediction

ASJC Scopus subject areas

  • Information Systems
  • Mathematical Physics
  • Physics and Astronomy (miscellaneous)
  • Electrical and Electronic Engineering


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