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.

翻译：时间序列预测由于存在多时间尺度上的复杂时序依赖性而面临重大挑战。本文提出ScatterFusion，一种新颖的框架，它将散射变换与分层注意力机制协同集成，以实现鲁棒的时间序列预测。我们的方法包含四个关键组成部分：(1) 分层散射变换模块（HSTM），用于提取捕获局部与全局模式的多尺度不变特征；(2) 尺度自适应特征增强（SAFE）模块，动态调整不同尺度上的特征重要性；(3) 多分辨率时序注意力（MRTA）机制，学习不同时间跨度上的依赖关系；(4) 趋势-季节-残差（TSR）分解引导的结构感知损失函数。在七个基准数据集上的大量实验表明，ScatterFusion优于其他常见方法，在各种预测时间跨度上均实现了误差指标的显著降低。