Identifying the factors of China's seasonal retail sales of consumer goods using a data grouping approach-based GRA method

Purpose Seasonal fluctuation interference often affects the relational analysis of economic time series. The main purpose of this paper is to propose a new grey relational model for relational analysis of seasonal time series and apply it to identify and eliminate the influence of seasonal fluctuation of retail sales of consumer goods in China. Design/methodology/approach First, the whole quarterly time series is divided into four groups by data grouping method. Each group only contains the time series data in the same quarter. Then, the new series of four-quarters are used to establish the grey correlation model and calculate its correlation coefficient. Finally, the correlation degree of factors in each group of data was calculated and sorted to determine its importance. Findings The data grouping method can effectively reflect the correlation between time series in different quarters and eliminate the influence of seasonal fluctuation. Originality/value This paper successfully realizes the application of grey relational model in quarterly time series and extends the applicable scope of grey relational model.