A Novel Hybrid Deep Learning Model for Sugar Price Forecasting Based on Time Series Decomposition
Jinlai Zhang, Yanmei Meng, Wei Jin, Jie Chen, Johnny Qin
Abstract
Sugar price forecasting has attracted extensive attention from policymakers due to its significant impact on people’s daily lives and markets. In this paper, we present a novel hybrid deep learning model that utilizes the merit of a time series decomposition technology empirical mode decomposition (EMD) and a hyperparameter optimization algorithm Tree of Parzen Estimators (TPEs) for sugar price forecasting. The effectiveness of the proposed model was implemented in a case study with the price of London Sugar Futures. Two experiments are conducted to verify the superiority of the EMD and TPE. Moreover, the specific effects of EMD and TPE are analyzed by the DM test and improvement percentage. Finally, empirical results demonstrate that the proposed hybrid model outperforms other models.