Copper Price Forecasting Based on Improved Least Squares Support Vector Machine with Butterfly Optimization Algorithm

Abstract

Copper prices are commonly used as indicators of economic development due to the increased operational risks of copper trading companies caused by their fluctuations and the effect on the government's ability to formulate market regulation policies. However, due to the high volatility of copper prices and resulting database discrepancies, traditional models exhibit lower accuracy and limited applicability. In this study, an improved hybrid prediction model based on the Butterfly Optimization Algorithm (BOA) and the Least Squares Support Vector Machine (LSSVM) is proposed. Firstly, the BOA is introduced to optimize the hyperparameters of the LSSVM. Then principal component analysis (PCA) is applied to data preprocessing, and the correlations of principal components are used to analyze and select model variables. To compare the forecasting accuracy and generalization ability based on the dataset of copper prices, some models are applied to establish multiple copper-price forecast cases, short-term, medium-term, and long-term. The results indicate that the PCA-BOA-LSSVM model demonstrates the most significant improvement, particularly in long-term forecasting cases. The highest optimization rate for RMSE reach 55.61%. The evaluation metrics of RMSE and MAPE for each case do not exceed 0.5 and 0.1, respectively, while R² remains above 0.6. In conclusion, this study provides a high-precision model for short-term, medium-term, and long-term forecasts of copper prices and provides reliable theoretical support for government policy adjustment and market investment.