Forecasting Nickel futures price based on the empirical wavelet transform and gradient boosting decision trees

Abstract To improve the prediction accuracy of futures price, this paper proposes a hybrid approach based on gradient boosting decision tree (GBDT), correlation analysis, and empirical wavelet transform (EWT) and applies it to predict the settlement price of the London Metal Exchange (LME) Nickel. Firstly, the Spearman correlation coefficient is adopted to analyze the relationship between each input variable and the settlement price. Then, the EWT likewise used to decompose each variable time series into an independent component. And those components are paralleled as new input variables. Next, the GBDT is carried for forecasting the settlement price of LME Nickel. To validate the effectiveness of the EWT, comparison experiments between EMD (empirical mode decomposition) and EWT are carried out. The decomposition results show that the EWT can correctly decompose the settlement price and avoid redundant components. To better measure the viability and efficiency of the proposed method, experiments are on three financial data sets from Tushare before predicting Nickel price. The various implementations are illustrated that the EWT–GBDT can achieve the best results no matter on which data set. Therefore, we have reason to believe that the EWT–GBDT is a useful algorithm to predict LME Nickel settlement prices.

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