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A novel grey forecasting model with generalised fractal derivative and its optimisation

Lina Jia (Institute of EduInfo Science and Engineering, Nanjing Normal University, Nanjing, China) (School of Information Science and Engineering, Zaozhuang University, Zaozhuang, China)
MingYong Pang (Institute of EduInfo Science and Engineering, Nanjing Normal University, Nanjing, China)

Grey Systems: Theory and Application

ISSN: 2043-9377

Article publication date: 30 April 2024

Issue publication date: 27 June 2024

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Abstract

Purpose

The purpose of this paper is to propose a new grey prediction model, GOFHGM (1,1), which combines generalised fractal derivative and particle swarm optimisation algorithms. The aim is to address the limitations of traditional grey prediction models in order selection and improve prediction accuracy.

Design/methodology/approach

The paper introduces the concept of generalised fractal derivative and applies it to the order optimisation of grey prediction models. The particle swarm optimisation algorithm is also adopted to find the optimal combination of orders. Three cases are empirically studied to compare the performance of GOFHGM(1,1) with traditional grey prediction models.

Findings

The study finds that the GOFHGM(1,1) model outperforms traditional grey prediction models in terms of prediction accuracy. Evaluation indexes such as mean squared error (MSE) and mean absolute error (MAE) are used to evaluate the model.

Research limitations/implications

The research study may have limitations in terms of the scope and generalisability of the findings. Further research is needed to explore the applicability of GOFHGM(1,1) in different fields and to improve the model’s performance.

Originality/value

The study contributes to the field by introducing a new grey prediction model that combines generalised fractal derivative and particle swarm optimisation algorithms. This integration enhances the accuracy and reliability of grey predictions and strengthens their applicability in various predictive applications.

Keywords

Acknowledgements

The work was supported by grants from the Post Graduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX23_1571), Nanjing Normal University Doctoral Dissertation Excellent Topic Funding Program (No. 18120000302227), Science and Technology Innovation 2030— major project of “New Generation Artificial Intelligence” (No. 2022ZD0115905), Jiangsu Province Education Science Planning Project (Key Project) “Multimodal Data-Driven Classroom Digital Twin Model Construction and Group Learning Behavior Analysis” (No. B/2023/01/96).

Citation

Jia, L. and Pang, M. (2024), "A novel grey forecasting model with generalised fractal derivative and its optimisation", Grey Systems: Theory and Application, Vol. 14 No. 3, pp. 543-560. https://doi.org/10.1108/GS-11-2023-0109

Publisher

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Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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