Accurate and efficient classification based on common principal components analysis for multivariate time series-Reference-Cited by-同舟云学术

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Accurate and efficient classification based on common principal components analysis for multivariate time series

Published:2016-01 Issue: Volume:171 Page:744-753
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Li Hailin^ORCID

Funder

National Natural Science Foundation of China

Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference30 articles.

1. Sparse moving maxima models for tail dependence in multivariate financial time series;Tang;J. Stat. Plan. Inference,2013

2. Electroencephalogram (eeg) time series classification;Chaovalitwongse;Ann. Oper. Res.,2006

3. Multivariate time series clustering on geophysical data recorded at mt. etna from 1996 to 2003;Di Salvo;J. Volcanol. Geotherm. Res.,2013

4. Induction of decision trees;Quinlan;Mach. Learn.,1986

5. J.R. Quinlan, Improved Use of Continuous Attributes in c4. 5, 1996, arXiv preprint cs/9603103.

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