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Time series regression based on Bayesian model averaging and principal component analysis

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DOI: 10.23977/acss.2023.070110 | Downloads: 17 | Views: 602

Author(s)

Jiayi Lu 1

Affiliation(s)

1 School of Urban, Xi'an Polytechnic University, Xi'an, Shaanxi, 710600, China

Corresponding Author

Jiayi Lu

ABSTRACT

This paper proposed an adaptive prediction model for high-dimensional time series data based on model averaging method and principal component analysis. Specifically, this paper considers the case where the response variable is a scalar and the predictor variable is a time series. Firstly, the high-dimensional time series data is extracted information by principal component analysis. Secondly, the Bayesian model averaging method is used to perform the forecast task based on the principal component projection matrix. The proposed method can effectively deal with the unsupervised nature of PCA and avoid the problem of selecting the number of PCA. It is demonstrated that the proposed method is competitive compared with the lasso regression and the ridge regression by real data analyses.

KEYWORDS

Time series data, high-dimensional problem, PCA, model averaging

CITE THIS PAPER

Jiayi Lu. Time series regression based on Bayesian model averaging and principal component analysis. Advances in Computer, Signals and Systems (2023) Vol. 7: 75-81. DOI: http://dx.doi.org/10.23977/acss.2023.070110.

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