A dynamic weighting adjustment algorithm for hybrid gray model based on artificial neural network. (2nd April 2020)

• Record Type: Journal Article
• Authors: Lee, Pin-Chan; Ye, Bo; Lo, Tzu-Ping; Long, Danbing
• Is Part Of: Journal of the Chinese Institute of Engineers. Volume 43:Number 3(2020)
• Page Start: 249
• Page End: 256
• Publication Date: 2020-04-02
• Subjects: Artificial neural network -- hybrid gray model -- time series -- weighting adjustment; Technology -- Periodicals; Engineering -- Periodicals; 620.005
• DOI: 10.1080/02533839.2019.1708802
• Languages: English
• ISSNs: 0253-3839

Abstract

ABSTRACT: Hybrid gray model is a combination of gray model and other mathematical models to obtain a high precision prediction. It has demonstrated good performance in many applications. However, there is little discussion on the optimal combination of weights among these models. For this purpose, this paper proposes a dynamic weighting hybrid gray model to provide a flexible combination to adapt to both stable and unstable time series. Short, medium and long term modeling numbers are used to verify the reliability of the proposed method. Two illustrative examples are shown to compare the prediction accuracy of the proposed method with that of the classical hybrid gray model and ANN model. Results show that the proposed model is better and is more adaptable to a time series with rapid changes.