Meteorological sequence prediction based on multivariate space-time auto regression model and fractional calculus grey model

Li Wang, Yuxin Xie, Xiaoyi Wang, Jiping Xu, Huiyan Zhang, Jiabin Yu, Qian Sun and Zhiyao Zhao

Chaos, Solitons & Fractals, 2019, vol. 128, issue C, 203-209

Abstract: Meteorological data is the basis for climate prediction and various scientific research. It is very important to study and prediction meteorological data. At present, the prediction methods for meteorological data are mainly a single intelligent method or a single point time series method based on time series data, which ignoring the interaction between meteorological sites and multiple meteorological factors. In this paper, Meteorological space-time data is decomposed into high frequency component and low frequency component by Hilbert Huang Transform. The high frequency component is modeling and predicting by fractional calculus grey model. The low frequency component is modeling and predicting by multivariate space-time auto regression model. The model verification results show that compared with the existing time series prediction methods, this method can more fully explain the non-stationary and nonlinear dynamic process of multivariate meteorological space-time sequences.

Keywords: Multivariate space-time auto regression model; Meteorological data; Prediction; Fractional calculus grey model (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:128:y:2019:i:c:p:203-209

DOI: 10.1016/j.chaos.2019.07.056

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