Multi-decomposition in deep learning models for futures price prediction

Abstract

In the literature pertaining to futures price prediction using decomposition ensemble algorithms, little has been discussed in regard to determining the optimal number of decompositions and the extent to which information should be integrated. To investigate the effectiveness of multiple price decompositions in improving the accuracy of deep learning models for futures prices, we conducted an empirical study on the daily closing prices of China's main contracts for gold, zinc, soybean meal, crude oil, iron ore, and PTA futures. We utilized a variety of prevalent deep learning models and two groups of benchmark models: shallow machine learning models and regression models. To split the prices into basic components, we proposed a VMD-EEMD technique and fed them to the models. By controlling the decomposition times and model types, we further studied the predictive performance of the deep learning models against benchmark models without decomposition or with only VMD. Our empirical evidence showed that multiple price decompositions, as an input engineering technique, proved to be an effective approach for enhancing the prediction accuracy of deep learning models applied to financial equities. The average percentages of error reduction of double decomposition against single decomposition are 6.13%, 9.45%, 7.09%, and 4.91% for the four common performance metrics: MAE, MSE, MAPE, and RMSE, respectively. Moreover, we explored the necessity of secondary decomposition and optimal decomposition times to balance prediction accuracy and model complexity. These findings provide important theoretical implications and practical guidance for researchers.