ScatterFusion: A Hierarchical Scattering Transform Framework for Enhanced Time Series Forecasting
This work addresses forecasting challenges for domains with complex temporal dependencies, presenting an incremental improvement through a novel hybrid method.
The paper tackled the problem of time series forecasting by introducing ScatterFusion, a framework integrating scattering transforms with hierarchical attention mechanisms, which outperformed other methods on seven benchmark datasets with significant error reductions.
Time series forecasting presents significant challenges due to the complex temporal dependencies at multiple time scales. This paper introduces ScatterFusion, a novel framework that synergistically integrates scattering transforms with hierarchical attention mechanisms for robust time series forecasting. Our approach comprises four key components: (1) a Hierarchical Scattering Transform Module (HSTM) that extracts multi-scale invariant features capturing both local and global patterns; (2) a Scale-Adaptive Feature Enhancement (SAFE) module that dynamically adjusts feature importance across different scales; (3) a Multi-Resolution Temporal Attention (MRTA) mechanism that learns dependencies at varying time horizons; and (4) a Trend-Seasonal-Residual (TSR) decomposition-guided structure-aware loss function. Extensive experiments on seven benchmark datasets demonstrate that ScatterFusion outperforms other common methods, achieving significant reductions in error metrics across various prediction horizons.