This article discusses a model of grey trend analysis and its uses. The authors test their methodology using real-world data in order to evaluate its results and its usefulness. The authors differentiate this method from the standard trend analysis and how the methodology goes beyond in its analytical value.
The authors begin with their differentiation and clarify the background of grey trend analysis. They explain in the article that grey trend analysis generally looks at the correlation space decomposition and changes between sequences. With this methodology, analysts can look at the proximity of rate change and their relationships. This kind of study is useful for looking deeper into analyzing trends.
The authors then explain and prove the math for grey change rate relational analysis (GCRA). Each use for the method has a formula and definitions that the authors explain in detail. The authors then test the methodology with a case study.
In the case study the authors use data collected from Fuxin's coal industrial cluster. The purpose of their study was to provide a predictor for coal cluster exhaustion. The data were collected to test the measure the different characteristics of coal that was to provide government entities to develop a plan to create more sustainable coal developments. Using multiple variables and trends, the researchers analyzed the original data and found key correlations between the trends to use as forecasting benchmarks.
The researchers concluded that the methodology was an effective means for generating and augmenting predictive analysis. The researchers explained the value of such analysis in that it goes farther than standard trend analysis to increase of the potential objects and factors available for study.
The authors could have tested the methodology more and introduced more potential examples for the method's application. The authors did not cover much of the criticisms of the methodology in order to more fully check their methods and encourage future analysis and test analyses.
Xuemei, L., Yaoguo, D., Lei, J., & Wenfang, K. (2015). GCRA model for grey trend analysis and its application. Journal Of Grey System, 27(1), 57-69.