A Hybrid Model for Copper Futures Price Forecasting Utilizing Complexity-Aware Variational Mode Decomposition and Reconstruction and Multi-Behavior-Triggered Interaction Modeling

TL;DR

This paper introduces a new forecasting model for copper futures prices that combines market data with behavioral signals to improve accuracy.

Contribution

The novel contribution is the MBTI-Net framework, which integrates behavioral and market data through a unified modeling approach.

Findings

MBTI-Net outperforms existing benchmarks in forecasting copper futures prices.

The model effectively captures behavior-driven dependencies using a complexity-aware reconstruction mechanism.

Volatility and behavior-aware normalization improves fusion of heterogeneous data sources.

Abstract

Accurate forecasting of copper futures prices is crucial for risk management and investment decisions. However, existing approaches primarily rely on historical prices and incorporate behavioral signals without a unified modeling framework. To address this limitation, we propose MBTI-Net (Multi-source Behavior-Triggered Interaction Network), a behavior-aware forecasting framework for heterogeneous copper market data. We first construct a compact behavioral factor from Baidu search indices via a multi-view projection strategy that preserves structural and predictive information. We then develop a complexity-aware reconstruction mechanism that aggregates intrinsic mode functions into multi-frequency components based on fuzzy entropy and energy. To accommodate distributional and volatility differences between behavioral and market variables, we introduce VB-ReVIN (Volatility- and Behavior-aware Reversible Instance Normalization). Building upon these representations, MBTI-Net models dynamic multi-source interactions triggered by behavioral intensity and market conditions, enabling adaptive cross-source information fusion. Experiments on LME and SHFE copper futures datasets demonstrate consistent improvements over state-of-the-art benchmarks, highlighting the importance of explicitly modeling behavior-driven dependencies in financial forecasting.

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