Application of Grey Relational Analysis for Multivariate Time Series

Abstract

Grey relational analysis (GRA) has been widely applied in analysing multivariate time series data (MTS). It is an alternate solution to the traditional statistical limitations. GRA is employed to search for grey relational grade (GRG) which can be used to describe the relationships between the data attributes and to determine the important factors that significantly influence some defined objectives. This paper demonstrates how GRA has been successfully used in identifying the significant factors that affect the grain crop yield in China from 1990 to 2003. The results from analysing the sample data revealed that the main factor that affects the trend of crop yield is the consumption of pesticide and chemical fertilizer and the least important factor to be considered is the agricultural labour. Thus, by properly adjusting the significant affecting factors, the China's crop yield performance can be further improved. Furthermore, GRA can provide a ranking scheme that gives the order of the grey relationship among the dependent and independent factors which leads to essential information such as which input factor need to be considered to forecast grain crop yield more precisely when using artificial neural network (ANN). In order to evaluate the performance of GRA in ANN model, a comparison is made using multiple linear regression (MR) statistical method. The results from the experiment show that ANN using GRA has outperformed the MR model with 99.0% in forecasting accuracy.