# 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.

## Key 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.