Department of Nursing, I-Shou University, Kaohsiung, Taiwan.
Department of Industrial Management, I-Shou University, Kaohsiung, Taiwan.
PLoS One. 2020 Oct 5;15(10):e0240065. doi: 10.1371/journal.pone.0240065. eCollection 2020.
Ever since the grey system theory was proposed about 40 years ago, its characteristics such as small samples, few data, and uncertainty have been used for study in the literature with increasingly wider scope. Recent studies on grey relation analysis have included static data analyses, and most of them have adopted initial values with only a relational order. Under the same study conditions, if different data preprocessing methods are used, then the relational order will be ranked differently. This study took Taiwan as the object to explore seven economic indices (birth rate (%), Taiwan's total population (thousand people), unemployment rate (%), income per capita (USD), weighted average interest rate on deposits (%), Consumer Price Index (CPI), and national income (NI)) and how they affect the economic growth rate. The traditional static grey relational analysis treated the collected data with taking consideration of time effect which is irrational under some circumstance. An innovative dynamic grey relational analysis was carried out by shifting the raw data due to the time leading or lagging effect which is a mean to improve the capability of traditional grey relational analysis. The differences in analyses between static grey relational analysis and dynamic grey relational analysis via different data preprocessing methods were further discussed, finding that different data preprocessing methods generated a new set of relational orders through the latter. Finally, the prosperity index was used to identify the effects of all factors on economic growth (leading, synchronization, and lagging indices).
自 40 年前灰色系统理论提出以来，其小样本、少数据、不确定性等特点在文献研究中得到了越来越广泛的应用。最近对灰色关联分析的研究包括静态数据分析，其中大多数采用只有关系顺序的初始值。在相同的研究条件下，如果使用不同的数据预处理方法，则关系顺序的排名将不同。本研究以台湾为对象，探讨了七个经济指标（出生率（%）、台湾总人口（千人）、失业率（%）、人均收入（美元）、存款加权平均利率（%）、消费者物价指数（CPI）和国民收入（NI））以及它们如何影响经济增长率。传统的静态灰色关联分析考虑了时间效应，在某些情况下这是不合理的。通过原始数据的移动，进行了创新性的动态灰色关联分析，这是一种改进传统灰色关联分析能力的方法。通过不同的数据预处理方法对静态灰色关联分析和动态灰色关联分析的分析差异进行了进一步讨论，发现不同的数据预处理方法通过后者产生了新的关系顺序。最后，使用繁荣指数来识别所有因素对经济增长的影响（领先、同步和滞后指数）。
