Can online search data improve the forecast accuracy of pork price in China?. (28th January 2020)

• Record Type: Journal Article
• Authors: Ling, Liwen; Zhang, Dabin; Chen, Shanying; Mugera, Amin W.
• Is Part Of: Journal of forecasting. Volume 39:Number 4(2020)
• Page Start: 671
• Page End: 686
• Publication Date: 2020-01-28
• Subjects: animal diseases -- Baidu index -- online search data -- pork price forecasting -- SVR; Forecasting -- Periodicals; Forecasting -- Mathematical models -- Periodicals; 003.2
• DOI: 10.1002/for.2649
• Languages: English
• ISSNs: 0277-6693

Abstract

Abstract: Online search data provide us with a new perspective for quantifying public concern about animal diseases, which can be regarded as a major external shock to price fluctuations. We propose a modeling framework for pork price forecasting that incorporates online search data with support vector regression model. This novel framework involves three main steps: that is, formulation of the animal diseases composite indexes (ADCIs) based on online search data; forecast with the original ADCIs; and forecast improvement with the decomposed ADCIs. Considering that there are some noises within the online search data, four decomposition techniques are introduced: that is, wavelet decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and singular spectrum analysis. The experimental study confirms the superiority of the proposed framework, which improves both the level and directional prediction accuracy. With the SSA method, the noise within the online search data can be removed and the performance of the optimal model is further enhanced. Owing to the long‐term effect of diseases outbreak on price volatility, these improvements are more prominent in the mid‐ and long‐term forecast horizons.