Pattern-guided forecasting framework for metal price prediction with grouping decomposed series

Abstract Accurate forecasting of precious metal prices is increasingly critical in modern financial markets, as these metals function as industrial commodities and as strategic financial instruments for portfolio diversification and risk management. Although recent advances in financial technology have produced a range of forecasting approaches from traditional econometric methods to sophisticated deep learning models—the complex dynamics of metal prices continue to challenge existing methodologies. This paper introduces a significant innovation in financial forecasting by revealing and leveraging previously unrecognized pattern relationships in decomposed time series data. Our comprehensive analysis of metal price dynamics reveals distinct grouped patterns in decomposed time series components, challenging the conventional assumption of independence in current forecasting methods. Based on these insights, we propose the pattern-guided forecasting framework (PGFF), which enhances forecasting accuracy by leveraging cross-dimensional pattern relationships in decomposed time series. Our framework employs a novel two-stage approach: first, categorizing decomposed time series based on their temporal characteristics and autocorrelation patterns; then, implementing cross-dimensional forecasting to capture complex market dynamics. Empirical analysis of four major precious metals demonstrates that PGFF consistently outperforms existing forecasting frameworks, offering significant implications for investment decision-making and portfolio management in modern financial markets.