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

N. Olspert; M. J. Kapyla; J. Pelt

doi:10.1109/BigData.2016.7840977

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

N. Olspert, M. J. Kapyla, J. Pelt

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

3 Sitaatiot (Scopus)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
Kustantaja	IEEE
Sivut	3214-3223
Sivumäärä	10
ISBN (elektroninen)	9781467390040
DOI - pysyväislinkit	https://doi.org/10.1109/BigData.2016.7840977
Tila	Julkaistu - 2 helmikuuta 2017
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	IEEE International Conference on Big Data - Washington, Yhdysvallat Kesto: 5 joulukuuta 2016 → 8 joulukuuta 2016 Konferenssinumero: 4

Conference

Conference	IEEE International Conference on Big Data
Maa	Yhdysvallat
Kaupunki	Washington
Ajanjakso	05/12/2016 → 08/12/2016

Pääsy asiakirjaan

10.1109/BigData.2016.7840977

Link to publication in Scopus

Sormenjälki Sukella tutkimusaiheisiin 'Method for estimating cycle lengths from multidimensional time series: Test cases and application to a massive 'in silico' dataset'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä

@inproceedings{924a7de0945a4e9283bb8c7696402a2b,

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

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.",

keywords = "Statistics, Time series analysis",

author = "N. Olspert and Kapyla, {M. J.} and J. Pelt",

year = "2017",

month = feb,

day = "2",

doi = "10.1109/BigData.2016.7840977",

language = "English",

pages = "3214--3223",

booktitle = "Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016",

publisher = "IEEE",

address = "United States",

note = "IEEE International Conference on Big Data ; Conference date: 05-12-2016 Through 08-12-2016",

}

Olspert, N , Kapyla, MJ & Pelt, J 2017, Method for estimating cycle lengths from multidimensional time series: Test cases and application to a massive 'in silico' dataset. julkaisussa Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016., 7840977, IEEE, Sivut 3214-3223, IEEE International Conference on Big Data, Washington, Yhdysvallat, 05/12/2016. https://doi.org/10.1109/BigData.2016.7840977

Method for estimating cycle lengths from multidimensional time series : Test cases and application to a massive 'in silico' dataset. / Olspert, N.; Kapyla, M. J.; Pelt, J.

Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. IEEE, 2017. s. 3214-3223 7840977.

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

TY - GEN

T1 - Method for estimating cycle lengths from multidimensional time series

T2 - IEEE International Conference on Big Data

AU - Olspert, N.

AU - Kapyla, M. J.

AU - Pelt, J.

N1 - Conference code: 4

PY - 2017/2/2

Y1 - 2017/2/2

N2 - 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.

AB - 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.

KW - Statistics

KW - Time series analysis

UR - http://www.scopus.com/inward/record.url?scp=85015231018&partnerID=8YFLogxK

U2 - 10.1109/BigData.2016.7840977

DO - 10.1109/BigData.2016.7840977

M3 - Conference contribution

AN - SCOPUS:85015231018

SP - 3214

EP - 3223

BT - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

PB - IEEE

Y2 - 5 December 2016 through 8 December 2016

ER -