A novel weighted fractional GM(1,1) model and its applications
type: Item
instance of: scholarly article
title: A novel weighted fractional GM(1,1) model and its applications
zbMATH Open document ID: 1432.93326
DOI: 10.1155/2020/6570683
author: Qin-Qin Shen
author: Quan Shi
author: Tian-Pei Tang
author: Lin-Quan Yao
published in: Complexity
publication date: 2020-02-17T00:00:00Z
review text: Summary: Based on the ideas of the new information priority principle and the fractional-accumulation generating operator, in this paper we propose a novel weighted fractional GM(1,1) (WFGM(1,1)) prediction model. In the new model, the original sequence is first transformed by using the weighted fractional-accumulation generating operator, which involves two parameters. With special choices of these parameters, the proposed WFGM(1,1) model reduces to the classical GM(1,1) model and the fractional GM(1,1) (FGM(1,1)) model, as well as the new information priority GM(1,1) (NIPGM(1,1)) model studied recently. Stability property of the WFGM(1,1) model is studied in detail. In practice, the quantum particle swarm optimization algorithm is adopted to choose the quasi-optimal parameters for the new model so as to get the best fitting accuracy. Finally, four numerical examples from different practical applications are present. Numerical results show that the new proposed prediction model is very efficient and has both the best fitting accuracy and the best prediction accuracy compared with the GM(1,1) and the FGM(1,1) as well as the NIPGM(1,1) prediction models.
Mathematics Subject Classification ID: 93E10
zbMATH DE Number: 7168423
describes a project that uses: fpp2
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2020/6570683
OpenAlex ID: W3001905237
Wikidata QID: Q126329448
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description: scientific article