Summary:
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.
Conclusion:
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.
Critique:
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.
Source:
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.
Posts
Briefly, by "Grey Trend Analysis" is that referring to the ability to analyze effectively the metadata of a situation and draw conclusions or a specific stand alone industry-specific conclusions like that found from the coal industry?
ReplyDeleteIt's more generalizable across multiple data sources and topics. More of an improvement on the standard methodology of trend analysis. To look and relate multiple trends.
ReplyDelete