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Nature of Computation and Communication. 8th EAI International Conference, ICTCC 2022, Vinh Long, Vietnam, October 27-28, 2022, Proceedings

Research Article

The Performance of a Kernel-Based Variable Dimension Reduction Method

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  • @INPROCEEDINGS{10.1007/978-3-031-28790-9_4,
        author={Thanh Do Van and Hai Nguyen Minh},
        title={The Performance of a Kernel-Based Variable Dimension Reduction Method},
        proceedings={Nature of Computation and Communication. 8th EAI International Conference, ICTCC 2022, Vinh Long, Vietnam, October 27-28, 2022, Proceedings},
        proceedings_a={ICTCC},
        year={2023},
        month={3},
        keywords={Time series Big data Nowcast Dimensional reduction Kernel trick Factor model},
        doi={10.1007/978-3-031-28790-9_4}
    }
    
  • Thanh Do Van
    Hai Nguyen Minh
    Year: 2023
    The Performance of a Kernel-Based Variable Dimension Reduction Method
    ICTCC
    Springer
    DOI: 10.1007/978-3-031-28790-9_4
Thanh Do Van, Hai Nguyen Minh,*
    *Contact email: nguyenminhhaidhcn@iuh.edu.vn

    Abstract

    Building forecast models, especially nowcast models, on large data sets of time series variables is a topic of great interest. The most popular method used to build such models is the dynamic factor model, in which factors are extracted from input data sets using the principal component analysis (PCA) or sparse PCA (SPCA) methods. Many studies have shown that the forecast accuracy of models built under such an approach is higher than that of other benchmark models. But the PCA and SPCA methods are only effective when input data sets approximate a hyperplane, while real-world data sets are not always so.

    The purpose of this article is to briefly introduce a kernel-based variable dimension reduction method called the KTPCA, and a process of trial and error of this method called the KTPCA#. Experimenting on real-world data sets at the same sampling frequency as well as mixed sampling frequencies shows that the KTPCA# method is superior to the PCA, SPCA, Randomized SPCA (RSPCA), and robust SPCA (ROBSPCA) methods.

    Keywords
    Time series Big data Nowcast Dimensional reduction Kernel trick Factor model
    Published
    2023-03-24
    Appears in
    SpringerLink
    http://dx.doi.org/10.1007/978-3-031-28790-9_4
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