Grey Relational Analysis And Its Application On Multivariate Time Series

Recently, grey relational analysis (GRA) has been widely applied in analyzing multivariate time series data (MTS); as a solution to the traditional statistical limitations. GRA, which has been proven to be simple and accurate method for analysing relationship in MTS are broadly used in various disciplines such as economics, sociology and engineering. GRA is employed to search for grey relational grade (GRG), which can be used to describe the relationships among the factors and to determine the important factors that significantly influence some defined objectives. Consequently, the aim of this paper is to demonstrate the application of GRA in determining the most influential affecting factors that affect the grain crop yield in China from 1990 to 2002. Additionally, a list of affecting factors that ordered according to their importance to crop yield were also determined. Analysis results of sample data showed that the main factor that affect the trend of crop yield are the consumption of pesticide and chemical fertilizer; while the least important factors need to be considered is the agriculture labour. Thus, by properly adjusting the significant affecting factors, the China’s crop yield performance can be improved efficiently. Furthermore, GRA can provide a ranking scheme that rank the order of the grey relationship among dependent and independent factors, and this allow us to decide which input factors need to be considered to forecast grain crop yield more precisely.