Dimension reduction for stationary multivariate time series data

Fayed Alshammri, Jiazhu Pan

Research output: Contribution to conferencePoster

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Abstract

Chang et al. (2016) extended PCA by finding a linear transformation of the original variables such that the transformed series is segmented into uncorrelated subseries with lower dimensions. This method is called TS-PCA. In our current research, we will extend TS-PCA by reducing the dimension of the transformed subseries further by applying GDPCA by Pena and Yohai (2016) to the results from TS-PCA, and possibly reach a further dimension reduction. Hence, the proposed method is a combination of TS-PCA and GDPCA.
Original languageEnglish
Number of pages1
Publication statusPublished - 19 May 2017
EventThe Education, Research, Humanities, and Statistics International Conference - Washington DC, United States
Duration: 19 May 201719 May 2019

Conference

ConferenceThe Education, Research, Humanities, and Statistics International Conference
Country/TerritoryUnited States
CityWashington DC
Period19/05/1719/05/19

Keywords

  • multivariate time series
  • TS-PCA
  • dimension reduction

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  • Best Talk Prize

    Alshammri, F. A. M. (Recipient), 2017

    Prize: Prize (including medals and awards)

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