Dimension reduction for stationary multivariate time series data

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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
CountryUnited States
CityWashington DC
Period19/05/1719/05/19

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Keywords

  • multivariate time series
  • TS-PCA
  • dimension reduction

Cite this

Alshammri, F., & Pan, J. (2017). Dimension reduction for stationary multivariate time series data. Poster session presented at The Education, Research, Humanities, and Statistics International Conference, Washington DC, United States.