Method for estimating cycle lengths from multidimensional time series: Test cases and application to a massive 'in silico' dataset

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

3 Citations (Scopus)

Abstract

Many real world systems exhibit cyclic behavior that is, for example, due to the nearly harmonic oscillations being perturbed by the strong fluctuations present in the regime of significant non-linearities. For the investigation of such systems special techniques relaxing the assumption to periodicity are required. In this paper, we present the generalization of one of such techniques, namely the D2 phase dispersion statistic, to multidimensional datasets, especially suited for the analysis of the outputs from three-dimensional numerical simulations of the full magnetohydrodynamic equations. We present the motivation and need for the usage of such a method with simple test cases, and present an application to a solar-like semi-global numerical dynamo simulation covering nearly 150 magnetic cycles.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
PublisherIEEE
Pages3214-3223
Number of pages10
ISBN (Electronic)9781467390040
DOIs
Publication statusPublished - 2 Feb 2017
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Big Data - Washington, United States
Duration: 5 Dec 20168 Dec 2016
Conference number: 4

Conference

ConferenceIEEE International Conference on Big Data
Country/TerritoryUnited States
CityWashington
Period05/12/201608/12/2016

Keywords

  • Statistics
  • Time series analysis

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