🤖 AI Summary
This work addresses the challenge of multi-scale time series forecasting, where modeling cross-scale patterns and maintaining computational efficiency are often at odds. The authors propose AWGformer, a novel architecture that integrates adaptive wavelet decomposition (AWDM) with a frequency-aware multi-head attention mechanism (FAMA), enabling dynamic selection of wavelet bases guided by signal characteristics and facilitating interaction among multi-band features. Coupled with cross-scale feature fusion (CSFF) and a hierarchical prediction network (HPN), AWGformer forms an end-to-end trainable framework supported by theoretical convergence guarantees. Extensive experiments on multiple benchmark datasets demonstrate that AWGformer significantly outperforms existing methods, achieving superior prediction accuracy and robustness—particularly in multi-scale and non-stationary scenarios.
📝 Abstract
Time series forecasting requires capturing patterns across multiple temporal scales while maintaining computational efficiency. This paper introduces AWGformer, a novel architecture that integrates adaptive wavelet decomposition with cross-scale attention mechanisms for enhanced multi-variate time series prediction. Our approach comprises: (1) an Adaptive Wavelet Decomposition Module (AWDM) that dynamically selects optimal wavelet bases and decomposition levels based on signal characteristics; (2) a Cross-Scale Feature Fusion (CSFF) mechanism that captures interactions between different frequency bands through learnable coupling matrices; (3) a Frequency-Aware Multi-Head Attention (FAMA) module that weights attention heads according to their frequency selectivity; (4) a Hierarchical Prediction Network (HPN) that generates forecasts at multiple resolutions before reconstruction. Extensive experiments on benchmark datasets demonstrate that AWGformer achieves significant average improvements over state-of-the-art methods, with particular effectiveness on multi-scale and non-stationary time series. Theoretical analysis provides convergence guarantees and establishes the connection between our wavelet-guided attention and classical signal processing principles.