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.
|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||The Education, Research, Humanities, and Statistics International Conference|
|Period||19/05/17 → 19/05/19|
- multivariate time series
- dimension reduction
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.