Feature Representation and Similarity Measure Based on Covariance Sequence for Multivariate Time Series

The high dimension of multivariate time series (MTS) is one of the major factors that impact on the efficiency and effectiveness of data mining. It has two kinds of dimensions, time-based dimensionality, and variable-based dimensionality. They often cause most of the algorithms and techniques applie...
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Autor*in:	Hailin Li [verfasserIn] Chunpei Lin [verfasserIn] Xiaoji Wan [verfasserIn] Zhengxin Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Multivariate time series covariance matrix principal component analysis data mining

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 67018-67026
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:67018-67026

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2915602

Katalog-ID:	DOAJ068530692

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520			\|a The high dimension of multivariate time series (MTS) is one of the major factors that impact on the efficiency and effectiveness of data mining. It has two kinds of dimensions, time-based dimensionality, and variable-based dimensionality. They often cause most of the algorithms and techniques applied to the field of MTS data mining to be a failure. In view of the importance of the correlation between any two variables in an MTS, the covariances between any two variables are applied to analyze the extraction of the features for every MTS. In this way, a covariance sequence can be constructed to represent the characteristic of the MTS. Furthermore, an excellent method of dimensionality reduction, principal component analysis (PCA), is used to extract the features of the covariance sequences that derived from an MTS dataset. Thus Euclidean distance is suitable to measure the similarity between the features fast. The experimental results demonstrate that the proposed method not only can handle multivariate time series with different lengths but also is more efficient and effective than the existing methods for the MTS data mining.
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allfields	10.1109/ACCESS.2019.2915602 doi (DE-627)DOAJ068530692 (DE-599)DOAJ80239e4973cf4776aa2293133ba21cf6 DE-627 ger DE-627 rakwb eng TK1-9971 Hailin Li verfasserin aut Feature Representation and Similarity Measure Based on Covariance Sequence for Multivariate Time Series 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The high dimension of multivariate time series (MTS) is one of the major factors that impact on the efficiency and effectiveness of data mining. It has two kinds of dimensions, time-based dimensionality, and variable-based dimensionality. They often cause most of the algorithms and techniques applied to the field of MTS data mining to be a failure. In view of the importance of the correlation between any two variables in an MTS, the covariances between any two variables are applied to analyze the extraction of the features for every MTS. In this way, a covariance sequence can be constructed to represent the characteristic of the MTS. Furthermore, an excellent method of dimensionality reduction, principal component analysis (PCA), is used to extract the features of the covariance sequences that derived from an MTS dataset. Thus Euclidean distance is suitable to measure the similarity between the features fast. The experimental results demonstrate that the proposed method not only can handle multivariate time series with different lengths but also is more efficient and effective than the existing methods for the MTS data mining. Multivariate time series covariance matrix principal component analysis data mining Electrical engineering. Electronics. Nuclear engineering Chunpei Lin verfasserin aut Xiaoji Wan verfasserin aut Zhengxin Li verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 67018-67026 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:67018-67026 https://doi.org/10.1109/ACCESS.2019.2915602 kostenfrei https://doaj.org/article/80239e4973cf4776aa2293133ba21cf6 kostenfrei https://ieeexplore.ieee.org/document/8709676/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 67018-67026
spelling	10.1109/ACCESS.2019.2915602 doi (DE-627)DOAJ068530692 (DE-599)DOAJ80239e4973cf4776aa2293133ba21cf6 DE-627 ger DE-627 rakwb eng TK1-9971 Hailin Li verfasserin aut Feature Representation and Similarity Measure Based on Covariance Sequence for Multivariate Time Series 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The high dimension of multivariate time series (MTS) is one of the major factors that impact on the efficiency and effectiveness of data mining. It has two kinds of dimensions, time-based dimensionality, and variable-based dimensionality. They often cause most of the algorithms and techniques applied to the field of MTS data mining to be a failure. In view of the importance of the correlation between any two variables in an MTS, the covariances between any two variables are applied to analyze the extraction of the features for every MTS. In this way, a covariance sequence can be constructed to represent the characteristic of the MTS. Furthermore, an excellent method of dimensionality reduction, principal component analysis (PCA), is used to extract the features of the covariance sequences that derived from an MTS dataset. Thus Euclidean distance is suitable to measure the similarity between the features fast. The experimental results demonstrate that the proposed method not only can handle multivariate time series with different lengths but also is more efficient and effective than the existing methods for the MTS data mining. Multivariate time series covariance matrix principal component analysis data mining Electrical engineering. Electronics. Nuclear engineering Chunpei Lin verfasserin aut Xiaoji Wan verfasserin aut Zhengxin Li verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 67018-67026 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:67018-67026 https://doi.org/10.1109/ACCESS.2019.2915602 kostenfrei https://doaj.org/article/80239e4973cf4776aa2293133ba21cf6 kostenfrei https://ieeexplore.ieee.org/document/8709676/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 67018-67026
allfields_unstemmed	10.1109/ACCESS.2019.2915602 doi (DE-627)DOAJ068530692 (DE-599)DOAJ80239e4973cf4776aa2293133ba21cf6 DE-627 ger DE-627 rakwb eng TK1-9971 Hailin Li verfasserin aut Feature Representation and Similarity Measure Based on Covariance Sequence for Multivariate Time Series 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The high dimension of multivariate time series (MTS) is one of the major factors that impact on the efficiency and effectiveness of data mining. It has two kinds of dimensions, time-based dimensionality, and variable-based dimensionality. They often cause most of the algorithms and techniques applied to the field of MTS data mining to be a failure. In view of the importance of the correlation between any two variables in an MTS, the covariances between any two variables are applied to analyze the extraction of the features for every MTS. In this way, a covariance sequence can be constructed to represent the characteristic of the MTS. Furthermore, an excellent method of dimensionality reduction, principal component analysis (PCA), is used to extract the features of the covariance sequences that derived from an MTS dataset. Thus Euclidean distance is suitable to measure the similarity between the features fast. The experimental results demonstrate that the proposed method not only can handle multivariate time series with different lengths but also is more efficient and effective than the existing methods for the MTS data mining. Multivariate time series covariance matrix principal component analysis data mining Electrical engineering. Electronics. Nuclear engineering Chunpei Lin verfasserin aut Xiaoji Wan verfasserin aut Zhengxin Li verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 67018-67026 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:67018-67026 https://doi.org/10.1109/ACCESS.2019.2915602 kostenfrei https://doaj.org/article/80239e4973cf4776aa2293133ba21cf6 kostenfrei https://ieeexplore.ieee.org/document/8709676/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 67018-67026
allfieldsGer	10.1109/ACCESS.2019.2915602 doi (DE-627)DOAJ068530692 (DE-599)DOAJ80239e4973cf4776aa2293133ba21cf6 DE-627 ger DE-627 rakwb eng TK1-9971 Hailin Li verfasserin aut Feature Representation and Similarity Measure Based on Covariance Sequence for Multivariate Time Series 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The high dimension of multivariate time series (MTS) is one of the major factors that impact on the efficiency and effectiveness of data mining. It has two kinds of dimensions, time-based dimensionality, and variable-based dimensionality. They often cause most of the algorithms and techniques applied to the field of MTS data mining to be a failure. In view of the importance of the correlation between any two variables in an MTS, the covariances between any two variables are applied to analyze the extraction of the features for every MTS. In this way, a covariance sequence can be constructed to represent the characteristic of the MTS. Furthermore, an excellent method of dimensionality reduction, principal component analysis (PCA), is used to extract the features of the covariance sequences that derived from an MTS dataset. Thus Euclidean distance is suitable to measure the similarity between the features fast. The experimental results demonstrate that the proposed method not only can handle multivariate time series with different lengths but also is more efficient and effective than the existing methods for the MTS data mining. Multivariate time series covariance matrix principal component analysis data mining Electrical engineering. Electronics. Nuclear engineering Chunpei Lin verfasserin aut Xiaoji Wan verfasserin aut Zhengxin Li verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 67018-67026 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:67018-67026 https://doi.org/10.1109/ACCESS.2019.2915602 kostenfrei https://doaj.org/article/80239e4973cf4776aa2293133ba21cf6 kostenfrei https://ieeexplore.ieee.org/document/8709676/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 67018-67026
allfieldsSound	10.1109/ACCESS.2019.2915602 doi (DE-627)DOAJ068530692 (DE-599)DOAJ80239e4973cf4776aa2293133ba21cf6 DE-627 ger DE-627 rakwb eng TK1-9971 Hailin Li verfasserin aut Feature Representation and Similarity Measure Based on Covariance Sequence for Multivariate Time Series 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The high dimension of multivariate time series (MTS) is one of the major factors that impact on the efficiency and effectiveness of data mining. It has two kinds of dimensions, time-based dimensionality, and variable-based dimensionality. They often cause most of the algorithms and techniques applied to the field of MTS data mining to be a failure. In view of the importance of the correlation between any two variables in an MTS, the covariances between any two variables are applied to analyze the extraction of the features for every MTS. In this way, a covariance sequence can be constructed to represent the characteristic of the MTS. Furthermore, an excellent method of dimensionality reduction, principal component analysis (PCA), is used to extract the features of the covariance sequences that derived from an MTS dataset. Thus Euclidean distance is suitable to measure the similarity between the features fast. The experimental results demonstrate that the proposed method not only can handle multivariate time series with different lengths but also is more efficient and effective than the existing methods for the MTS data mining. Multivariate time series covariance matrix principal component analysis data mining Electrical engineering. Electronics. Nuclear engineering Chunpei Lin verfasserin aut Xiaoji Wan verfasserin aut Zhengxin Li verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 67018-67026 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:67018-67026 https://doi.org/10.1109/ACCESS.2019.2915602 kostenfrei https://doaj.org/article/80239e4973cf4776aa2293133ba21cf6 kostenfrei https://ieeexplore.ieee.org/document/8709676/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 67018-67026
language	English
source	In IEEE Access 7(2019), Seite 67018-67026 volume:7 year:2019 pages:67018-67026
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topic_facet	Multivariate time series covariance matrix principal component analysis data mining Electrical engineering. Electronics. Nuclear engineering
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authorswithroles_txt_mv	Hailin Li @@aut@@ Chunpei Lin @@aut@@ Xiaoji Wan @@aut@@ Zhengxin Li @@aut@@
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callnumber-first	T - Technology
author	Hailin Li
spellingShingle	Hailin Li misc TK1-9971 misc Multivariate time series misc covariance matrix misc principal component analysis misc data mining misc Electrical engineering. Electronics. Nuclear engineering Feature Representation and Similarity Measure Based on Covariance Sequence for Multivariate Time Series
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topic_title	TK1-9971 Feature Representation and Similarity Measure Based on Covariance Sequence for Multivariate Time Series Multivariate time series covariance matrix principal component analysis data mining
topic	misc TK1-9971 misc Multivariate time series misc covariance matrix misc principal component analysis misc data mining misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Multivariate time series misc covariance matrix misc principal component analysis misc data mining misc Electrical engineering. Electronics. Nuclear engineering
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title	Feature Representation and Similarity Measure Based on Covariance Sequence for Multivariate Time Series
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title_full	Feature Representation and Similarity Measure Based on Covariance Sequence for Multivariate Time Series
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title_sort	feature representation and similarity measure based on covariance sequence for multivariate time series
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title_auth	Feature Representation and Similarity Measure Based on Covariance Sequence for Multivariate Time Series
abstract	The high dimension of multivariate time series (MTS) is one of the major factors that impact on the efficiency and effectiveness of data mining. It has two kinds of dimensions, time-based dimensionality, and variable-based dimensionality. They often cause most of the algorithms and techniques applied to the field of MTS data mining to be a failure. In view of the importance of the correlation between any two variables in an MTS, the covariances between any two variables are applied to analyze the extraction of the features for every MTS. In this way, a covariance sequence can be constructed to represent the characteristic of the MTS. Furthermore, an excellent method of dimensionality reduction, principal component analysis (PCA), is used to extract the features of the covariance sequences that derived from an MTS dataset. Thus Euclidean distance is suitable to measure the similarity between the features fast. The experimental results demonstrate that the proposed method not only can handle multivariate time series with different lengths but also is more efficient and effective than the existing methods for the MTS data mining.
abstractGer	The high dimension of multivariate time series (MTS) is one of the major factors that impact on the efficiency and effectiveness of data mining. It has two kinds of dimensions, time-based dimensionality, and variable-based dimensionality. They often cause most of the algorithms and techniques applied to the field of MTS data mining to be a failure. In view of the importance of the correlation between any two variables in an MTS, the covariances between any two variables are applied to analyze the extraction of the features for every MTS. In this way, a covariance sequence can be constructed to represent the characteristic of the MTS. Furthermore, an excellent method of dimensionality reduction, principal component analysis (PCA), is used to extract the features of the covariance sequences that derived from an MTS dataset. Thus Euclidean distance is suitable to measure the similarity between the features fast. The experimental results demonstrate that the proposed method not only can handle multivariate time series with different lengths but also is more efficient and effective than the existing methods for the MTS data mining.
abstract_unstemmed	The high dimension of multivariate time series (MTS) is one of the major factors that impact on the efficiency and effectiveness of data mining. It has two kinds of dimensions, time-based dimensionality, and variable-based dimensionality. They often cause most of the algorithms and techniques applied to the field of MTS data mining to be a failure. In view of the importance of the correlation between any two variables in an MTS, the covariances between any two variables are applied to analyze the extraction of the features for every MTS. In this way, a covariance sequence can be constructed to represent the characteristic of the MTS. Furthermore, an excellent method of dimensionality reduction, principal component analysis (PCA), is used to extract the features of the covariance sequences that derived from an MTS dataset. Thus Euclidean distance is suitable to measure the similarity between the features fast. The experimental results demonstrate that the proposed method not only can handle multivariate time series with different lengths but also is more efficient and effective than the existing methods for the MTS data mining.
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title_short	Feature Representation and Similarity Measure Based on Covariance Sequence for Multivariate Time Series
url	https://doi.org/10.1109/ACCESS.2019.2915602 https://doaj.org/article/80239e4973cf4776aa2293133ba21cf6 https://ieeexplore.ieee.org/document/8709676/ https://doaj.org/toc/2169-3536
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Feature Representation and Similarity Measure Based on Covariance Sequence for Multivariate Time Series

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