A dynamic ensemble learning approach with spectral clustering for beef and lamb prices prediction

Abstract

The price gap between the prices of beef and lamb in Qinghai and the average national price of beef and lamb persists and even tends to expand due to the contradiction between supply and demand, which is not only detrimental to the enhancement of income of Qinghai herders but also the healthy development of the beef and lamb industry in Qinghai. To better manage the prices of beef and lamb in Qinghai, to increase the income of Qinghai herders, and to build "a green and organic agricultural and livestock products exporting place" in Qinghai, an effective price forecasting model needs to be constructed and applied in Qinghai. Accordingly, we propose a data-driven ensemble forecasting model based on the idea of spectral clustering to forecast the future prices trend of beef and lamb in Qinghai and hope to provide a theoretical basis for the scientific and healthy development of the Qinghai livestock industry. Specifically, this paper analyzed the fluctuation pattern and characteristics of beef and lamb prices in Qinghai through the spectral clustering method, and extracted their data fluctuation features, respectively. And these features are correspondingly embedded into the proposed ensemble forecasting model to predict the future prices trend of beef and lamb in Qinghai, respectively. The competitiveness of the proposed model was verified on about 14 years’ weekly Qinghai beef and lamb prices (RMB yuan/Kg) data which was obtained from the website of National Animal Husbandry Services using web crawler technology. The experimental results show that the proposed model can significantly improve the prediction accuracy of beef and lamb prices in Qinghai, and provide strong support for the management of beef and lamb prices in Qinghai, the realization of increased income of Qinghai herders, and the building of "a green and organic agricultural and livestock products exporting place" in Qinghai.