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

Olspert, N., Käpylä, M. J., & Pelt, J. (2016). Method for estimating cycle lengths from multidimensional time series: Test cases and application to a massive "in silico" dataset. In 2016 IEEE International Conference on Big Data (Big Data) (pp. 3214-3223).

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-C2A4-0 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-E43A-0
Genre: Conference Paper

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 Creators:
Olspert, N., Author
Käpylä, M. J.1, 2, Author              
Pelt, J., Author
Affiliations:
1Max Planck Research Group in Solar and Stellar Magnetic Activity, Max Planck Institute for Solar System Research, Max Planck Society, ou_2265638              
2Department Sun and Heliosphere, Max Planck Institute for Solar System Research, Max Planck Society, ou_1832289              

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 MPIS_GROUPS: Research Group Solar Stellar Mag Activity
 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.

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Language(s): eng - English
 Dates: 2016
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/BigData.2016.7840977
BibTex Citekey: Olspert:2016-152
 Degree: -

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Title: 2016 IEEE International Conference on Big Data (Big Data)
Place of Event: Washington, DC
Start-/End Date: 2016-12-05 - 2016-12-08

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Title: 2016 IEEE International Conference on Big Data (Big Data)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 3214 - 3223 Identifier: ISBN: 978-1-4673-9005-7