Influence of sample length on gray fuzzy prediction performance

Issue title: Cross-domain Applications of Fuzzy Logic and Machine Learning

Guest editors: Ekaterina Isaeva and Álvaro Rocha

Article type: Research Article

Authors: Wang, Jianzhong^a | Gao, Yashuo^b | Jin, Jian^{b; *}

Affiliations: [a] School of Business, Hebei Agriculture University, P.R. China | [b] School of Economics, Hebei University, P.R. China

Correspondence: [*] Corresponding author. Jian Jin, School of Economics, Hebei University, China. E-mail: [email protected].

Abstract:  Gray fuzzy prediction model is suitable for small-sample-size prediction. The real per capita disposable income of urban residents in Hebei Province used as an example, and samples 3–35 in length selected, the influence of sample length on prediction performance of the GM (1,1) model were investigated. Sample length presents a nonlinear relationship with the predicted relative error of the model. Compared with large samples with lengths more than 15, small samples with lengths below 15 are suitable to establish the gray fuzzy prediction model. Small samples with length of 8–13 are applicable to three-step prediction. Sample lengths suitable for modeling were proposed, and the above conclusions provide a certain theoretical foundation and guidance for the research and application of gray fuzzy prediction in the future.

Keywords: Sample size, predictive performance, grey prediction, GM(1,1), real per capita disposable income

DOI: 10.3233/JIFS-179752

Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 6, pp. 6745-6754, 2020