Dimension reduction for stationary multivariate time series data

Alshammri, Fayed and Pan, Jiazhu (2017) Dimension reduction for stationary multivariate time series data. In: The Education, Research, Humanities, and Statistics International Conference, 2017-05-19 - 2019-05-19.

[thumbnail of Alshammri-Pan-ERHS2017-Dimension-reduction-for-stationary-multivariate-time-series-data]
Preview
 Text. Filename: Alshammri_Pan_ERHS2017_Dimension_reduction_for_stationary_multivariate_time_series_data.pdf
Final Published Version
Download (1MB)| Preview

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.

ORCID iDs

Alshammri, Fayed and Pan, Jiazhu ORCID logoORCID: https://orcid.org/0000-0001-7346-2052;
CORE (COnnecting REpositories)