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 language | English |
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Number of pages | 1 |
Publication status | Published - 19 May 2017 |
Event | The Education, Research, Humanities, and Statistics International Conference - Washington DC, United States Duration: 19 May 2017 → 19 May 2019 |
Conference
Conference | The Education, Research, Humanities, and Statistics International Conference |
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Country/Territory | United States |
City | Washington DC |
Period | 19/05/17 → 19/05/19 |
Keywords
- multivariate time series
- TS-PCA
- dimension reduction