Fast time sequence data mining algorithm based on grey system theory

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Authors:

Hongyi Cao, Xi’an Medical University, Xi’an 710021, Shaanxi, China

Junhui Yang, Xi’an Medical University, Xi’an 710021, Shaanxi, China

Li Wang, Xi’an Medical University, Xi’an 710021, Shaanxi, China

Abstract:

Purpose. With the development of the big data technology, the time sequence data mining has become a hot spot that attracts the attention of the public. Based on the correlation and cooperativity of the time sequence data, we propose the fast time sequence data mining model based on the grey system theory.

Methodology. The correlation determination method that is based on the features of the relevant coefficient of the time shift sequence is obtained. As a result, a kind of fast time sequence data mining model based on the grey system theory is proposed.

Findings. The correlation determination methodology proposed in this paper is more effective than the Pearson linear correlation coefficient, Spearman rank correlation coefficient, Kendall rank correlation coefficient and Granger causality test.

Originality. In this paper, the double sequence fast correlation determination method and curve alignment method are provided. So far, we have not found other literature on the related research.

Practical value. The research results can provide theoretical basis for the determination of the correlation of regression analysis and the time alignment.

References/Список літератури

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