ScatterFusion：一种用于增强时间序列预测的分层散射变换框架
ScatterFusion: A Hierarchical Scattering Transform Framework for Enhanced Time Series Forecasting
AI总结：
ScatterFusion通过结合散射变换与分层注意力机制，提升时间序列预测的鲁棒性和准确性。
摘要：
时间序列预测由于在多个时间尺度上复杂的时序依赖性而面临重大挑战。本文介绍了ScatterFusion，一种新颖的框架，通过将散射变换与分层注意力机制相结合，实现稳健的时间序列预测。我们的方法包括四个关键组成部分：(1) 分层散射变换模块（HSTM），该模块提取多尺度不变特征，捕捉局部和全局模式；(2) 适应性特征增强模块（SAFE），该模块在不同尺度上动态调整特征重要性；(3) 多分辨率时间注意力机制（MRTA），该机制在不同时间范围内学习依赖关系；(4) 由趋势-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广泛实验表明，ScatterFusion优于其他常见方法，在各种预测时间范围内实现了显著的误差指标减少。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
