An optimized hybrid model based on artificial intelligence for grape price forecasting. Issue 12 (21st November 2019)

• Record Type: Journal Article
• Authors: Chu, Xiaoquan; Li, Yue; Tian, Dong; Feng, Jianying; Mu, Weisong
• Is Part Of: British food journal. Volume 121:Issue 12(2019)
• Page Start: 3247
• Page End: 3265
• Publication Date: 2019-11-21
• Subjects: Support vector machine -- Price predicting -- Optimized hybrid model -- Ensemble empirical mode decomposition -- Multiple linear regression; Food industry and trade -- Periodicals; Food -- Marketing -- Periodicals; Food adulteration and inspection -- Periodicals; Food -- Periodicals; 381.456413
• DOI: 10.1108/BFJ-06-2019-0390
• Languages: English
• ISSNs: 0007-070X

Abstract

Abstract : Purpose: The purpose of this paper is to propose an optimized hybrid model based on artificial intelligence methods, use the method of time series forecasting, to deal with the price prediction issue of China's table grape. Design/methodology/approach: The approaches follows the framework of "decomposition and ensemble, " using ensemble empirical mode decomposition (EEMD) to optimize the conventional price forecasting methods, and, integrating the multiple linear regression and support vector machine to build a hybrid model which could be applied in solving price series predicting problems. Findings: The proposed EEMD-ADD optimized hybrid model is validated to be considered satisfactory in a case of China' grape price forecasting in terms of its statistical measures and prediction performance. Practical implications: This study would resolve the difficulties in grape price forecasting and provides an adaptive strategy for other agricultural economic predicting problems as well. Originality/value: The paper fills the vacancy of concerning researches, proposes an optimized hybrid model integrating both classical econometric and artificial intelligence models to forecast price using time series method.