ScatterFusion: A Hierarchical Scattering Transform Framework for Enhanced Time Series Forecasting

• URL: http://arxiv.org/abs/2601.20401v1
• Date: Wed, 28 Jan 2026 09:06:01 GMT
• Title: ScatterFusion: A Hierarchical Scattering Transform Framework for Enhanced Time Series Forecasting
• Authors: Wei Li,
• Abstract summary: Time series forecasting presents significant challenges due to the complex temporal dependencies at multiple time scales.<n>This paper introduces ScatterFusion, a novel framework that integrates scattering transforms with hierarchical attention mechanisms for robust time series forecasting.
• Score: 3.453296006042559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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