A non-ferrous metal price ensemble prediction system based on innovative combined kernel extreme learning machine and chaos theory

Abstract

Non-ferrous metal futures, as a significant component of the financial market, are complementary and coordinated with other financial elements, which has been a key area of research in recent years. However, given the apparent volatility and chaotic nature of the non-ferrous metal price sequence, forecasting it remains a difficult challenge. While prior research employed a variety of methodologies to forecast metal prices, they overlooked the critical role of chaos feature analysis and the necessity of error analysis, severely limiting prediction accuracy. This paper designs a novel non-ferrous metal price ensemble prediction system that incorporates data decomposition, phase space reconstruction, multi-objective optimization, point prediction, and interval prediction. A combined kernel extreme learning machine based on the improved multi-objective lion swarm optimization algorithm is developed and theoretically explained to improve prediction accuracy and reliability. Additionally, the appropriate creation of the prediction interval based on the best-fit distribution of the point prediction error enabled the examination of various levels of uncertainty. In an empirical experiment using copper and aluminum prices from the London Metal Exchange, the proposed system demonstrated benefits in point and interval prediction, providing decision makers with useful prediction references.