Decomposition-Enhanced Network for Financial Time Series Forecasting

The extreme non-stationarity, high noise levels, and multi-timescale coupling in financial futures markets pose major challenges for time series forecasting. Existing models often struggle to disentangle localized shocks from global trends due to incompatible inductive biases. To address this issue, we propose a Decomposition-Enhanced Network (DENet). Following a divide-and-conquer paradigm, DENet adopts a multi-stream architecture: the main path extracts stable trends via moving averages and dual-path linear projections, while Auxiliary Stream I captures seasonal and local cyclical patterns using depthwise separable convolutions, and Auxiliary Stream II models high-frequency dynamics through a nonlinear autoregressive-style mapping. These components are integrated via an adaptive fusion mechanism, balancing global robustness and local structural sensitivity. Experiments on real-world futures data demonstrate that DENet outperforms a wide range of state-of-the-art benchmarks. Compared with DLinear, PatchTST, TSMixer, and iTransformer, DENet achieves an average reduction of 12.84% in RMSE for daily forecasting and 9.35% in MAE over the 5-minute, 12-step forecasting horizon on iron ore futures. Furthermore, we integrate DENet's dual-scale predictions into the R-Breaker strategy with parameter switching and dynamic position sizing. Backtesting results show that the annualized return for silicomanganese futures increases by 121.16 percentage points over the baseline strategy. Ultimately, DENet effectively bridges advanced structural modeling and actionable algorithmic trading.