A Combined Prediction Model for Hog Futures Prices Based on WOA-LightGBM-CEEMDAN

Xiang Wang,Shen Gao,Shiyu Zhou,Yibin Guo,Daqing Wu,Yonghui Duan,Ning Cai

doi:10.1155/2022/3216036

Abstract

An integrated hog futures price forecasting model based on whale optimization algorithm (WOA), LightGBM, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed to overcome the limitations of a single machine learning model with low prediction accuracy and insufficient model stability. The simulation process begins with a grey correlation analysis of the hog futures price index system in order to identify influencing factors; after that, the WOA-LightGBM model is developed, and the WOA algorithm is used to optimize the LightGBM model parameters; and, finally, the residual sequence is decomposed and corrected by using the CEEMDAN method to build a combined WOA-LightGBM-CEEMDAN model. Furthermore, it is used for comparison experiments to check the validity of the model by using data from CSI 300 stock index futures. Based on all experimental results, the proposed combined model shows the highest prediction accuracy, surpassing the comparative model. The model proposed in this study is accurate enough to meet the forecasting accuracy requirements and provides an effective method for forecasting future prices.

Highlights

Wang et al [28] combined CEEMDAN decomposition method and GRU neural network to predict natural gas price. e CEEMDAN decomposition method was used by Zhao and Chen [29] to decompose the carbon price, and the extreme learning machine (ELM) model was optimized with the improved sparrow algorithm to forecast each intrinsic mode functions (IMF) component, and the results showed that the combined approach can effectively improve the forecasting accuracy
A combined forecasting model of hog futures prices is developed by using whale optimization algorithm (WOA)-LightGBM and CEEMDAN, which addresses the shortcomings of single machine learning models in terms of forecasting accuracy and model stability
We decompose and correct the prediction residual series of the WOA-LightGBM model using the CEEMDAN method in order to construct a combined WOA-LightGBMCEEMDAN prediction model. e following conclusions were reached as a result of simulation experiments on hog futures price data

Summary

Introduction

China’s futures market has grown rapidly in recent years. As a form of risky investment and risky return, futures trading is a very important investment tool for investors. The futures market can reasonably use and gather a vast amount of social idle capital, which is valuable to China’s market economy. For both companies and investors, an accurate prediction of prices in the futures market is a key guide. Due to the fact that commodity futures are influenced by a variety of factors, which can cause large fluctuations in price, it is difficult to achieve accurate price control. Erefore, the accurate prediction of futures prices has become a hot research topic Due to the fact that commodity futures are influenced by a variety of factors, which can cause large fluctuations in price, it is difficult to achieve accurate price control. erefore, the accurate prediction of futures prices has become a hot research topic

Methods

Results

Conclusion