A Study on Metal Futures Price Prediction Based on Piecewise Cubic Bézier Filtering for TCN

Abstract

This study develops an effective forecasting model for metal futures prices with enhanced capability in trend identification and abrupt change detection, aiming to improve decision-making in both financial and industrial contexts. A hybrid framework is proposed that integrates non-uniform piecewise cubic Bézier curves with a temporal convolutional network (TCN). The Bézier–Hurst (BH) decomposition extracts multi-scale trend components, which are then processed by a TCN to capture long-range dependencies. Empirical results show that the model outperforms LSTM, standard TCN, Bézier–TCN, and WD-TCN, achieving higher accuracy in trend detection and abrupt change response. This integration of Bézier-based decomposition with TCN offers a novel and robust tool for forecasting, providing valuable support for risk control and strategic planning in commodity markets.