A new adaptive grey seasonal model for time series forecasting tasks

Abstract

Purpose

This paper intends to construct a new adaptive grey seasonal model (AGSM) to promote the application of the grey forecasting model in quarterly GDP.

Design/methodology/approach

Firstly, this paper constructs a new accumulation operation that embodies the new information priority by using a hyperparameter. Then, a new AGSM is constructed by using a new grey action quantity, nonlinear Bernoulli operator, discretization operation, moving average trend elimination method and the proposed new accumulation operation. Subsequently, the marine predators algorithm is used to quickly obtain the hyperparameters used to build the AGSM. Finally, comparative analysis experiments and ablation experiments based on China's quarterly GDP confirm the validity of the proposed model.

Findings

AGSM can be degraded to some classical grey prediction models by replacing its own structural parameters. The proposed accumulation operation satisfies the new information priority rule. In the comparative analysis experiments, AGSM shows better prediction performance than other competitive algorithms, and the proposed accumulation operation is also better than the existing accumulation operations. Ablation experiments show that each component in the AGSM is effective in enhancing the predictive performance of the model.

Originality/value

A new AGSM with new information priority accumulation operation is proposed.

Citation

Wang, R., Xu, Y. and Yang, Q. (2024), "A new adaptive grey seasonal model for time series forecasting tasks", Grey Systems: Theory and Application, Vol. 14 No. 2, pp. 360-373. https://doi.org/10.1108/GS-07-2023-0055