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.

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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.