A Novel System Based on Selection Strategy and Ensemble Mode for Non-Ferrous Metal Futures Market Management

Abstract

Non-ferrous metals, as one of the representative commodities with large international circulation, are of great significance to social and economic development. The time series of its prices are highly volatile and nonlinear, which makes metal price forecasting still a tough and challenging task. However, the existing research focus on the application of the individual advanced model, neglecting the in-depth analysis and mining of a certain type of model. In addition, most studies overlook the importance of sub-model selection and ensemble mode in metal price forecasting, which can lead to poor forecasting results under some circumstances. To bridge these research gaps, a novel forecasting system including data pretreatment module, sub-model forecasting module, model selection module, and ensemble module, which successfully introduces a nonlinear ensemble mode and combines the optimal sub-model selection method, is developed for the non-ferrous metal prices futures market management. More specifically, data pretreatment is carried out to capture the main features of metal prices to effectively mitigate those challenges caused by noise. Then, the extreme learning machine series models are employed as the sub-model library and employed to predict the decomposed sub-sequences. Moreover, an optimal sub-model selection strategy is implemented according to the newly proposed comprehensive index to select the best model for each sub-sequence. Then, by proposing a nonlinear ensemble forecasting mode, the final point forecasting and uncertainty interval forecasting results are obtained based on the forecasting results of the optimal sub-model. Experimental simulations are carried out using the datasets copper and zinc, which show that the present system is superior to other benchmarks. Therefore, the system can be used not only as an effective technique for non-ferrous metal prices futures market management but also as an alternative for other forecasting applications.