AWGformer: Adaptive Wavelet-Guided Transformer for Multi-Resolution Time Series Forecasting

• URL: http://arxiv.org/abs/2601.20409v1
• Date: Wed, 28 Jan 2026 09:14:22 GMT
• Title: AWGformer: Adaptive Wavelet-Guided Transformer for Multi-Resolution Time Series Forecasting
• Authors: Wei Li,
• Abstract summary: Time series forecasting requires capturing patterns across multiple temporal scales.<n>This paper introduces AWGformer, a novel architecture that integrates adaptive wavelet decomposition with cross-scale attention mechanisms.
• Score: 3.453296006042559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• TFWaveFormer: Temporal-Frequency Collaborative Multi-level Wavelet Transformer for Dynamic Link Prediction [13.707047958676482]
  We propose TFWaveFormer, a novel Transformer architecture that integrates temporal-frequency analysis with wavelet decomposition to enhance dynamic link prediction.<n>Our framework comprises three key components: (i) a temporal-frequency coordination mechanism that jointly models temporal and spectral representations, (ii) a learnable multi-resolution wavelet decomposition module that adaptively extracts multi-scale temporal patterns through parallel convolutions, and (iii) a hybrid Transformer module that effectively fuses local wavelet features with global temporal dependencies.
  arXiv  Detail & Related papers  (2026-03-04T11:47:57Z)
• DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters [50.43534351968113]
  Existing generative time series models do not address the multi-dimensional properties of time series data well.<n>Inspired by Multimodal Diffusion Transformers that integrate textual guidance into video generation, we propose Diffusion Transformers for Time Series (DiTS)
  arXiv  Detail & Related papers  (2026-02-06T10:48:13Z)
• Improving Day-Ahead Grid Carbon Intensity Forecasting by Joint Modeling of Local-Temporal and Cross-Variable Dependencies Across Different Frequencies [3.1953619915392246]
  Accurate forecasting of the grid carbon intensity factor (CIF) is critical for enabling demand-side management and reducing emissions in modern electricity systems.<n>Despite advances in deep learning-based methods, it remains challenging to capture the fine-grained local-temporal dependencies.<n>We propose a novel model that integrates two parallel modules.
  arXiv  Detail & Related papers  (2026-01-10T11:20:55Z)
• FusAD: Time-Frequency Fusion with Adaptive Denoising for General Time Series Analysis [92.23551599659186]
  Time series analysis plays a vital role in fields such as finance, healthcare, industry, and meteorology.<n>FusAD is a unified analysis framework designed for diverse time series tasks.
  arXiv  Detail & Related papers  (2025-12-16T04:34:27Z)
• WaveTuner: Comprehensive Wavelet Subband Tuning for Time Series Forecasting [22.28753360953834]
  We propose a Wavelet decomposition framework empowered by full-spectrum subband Tuning for time series forecasting.<n>WaveTuner comprises two key modules: (i) Adaptive Wavelet Refinement module, that transforms time series into time-frequency coefficients, and (ii) Multi-Branch module, that employs multiple functional branches, each instantiated as a flexible Kolmogorov-Arnold Network (KAN) with a distinct functional order to model a specific spectral subband.
  arXiv  Detail & Related papers  (2025-11-24T07:33:35Z)
• AWEMixer: Adaptive Wavelet-Enhanced Mixer Network for Long-Term Time Series Forecasting [12.450099337354017]
  We propose AWEMixer, an Adaptive Wavelet-Enhanced Mixer Network.<n>A Frequency Router designs to utilize the global periodicity pattern achieved by Fast Fourier Transform to adaptively weight localized wavelet subband.<n>A Coherent Gated Fusion Block to achieve selective integration of prominent frequency features with multi-scale temporal representation.
  arXiv  Detail & Related papers  (2025-11-06T11:27:12Z)
• WaveSeg: Enhancing Segmentation Precision via High-Frequency Prior and Mamba-Driven Spectrum Decomposition [61.3530659856013]
  We propose a novel decoder architecture, WaveSeg, which jointly optimize feature refinement in spatial and wavelet domains.<n>High-frequency components are first learned from input images as explicit priors to reinforce boundary details.<n>Experiments on standard benchmarks demonstrate that WaveSeg, leveraging wavelet-domain frequency prior with Mamba-based attention, consistently outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-10-24T01:41:31Z)
• A Unified Frequency Domain Decomposition Framework for Interpretable and Robust Time Series Forecasting [81.73338008264115]
  Current approaches for time series forecasting, whether in the time or frequency domain, predominantly use deep learning models based on linear layers or transformers.<n>We propose FIRE, a unified frequency domain decomposition framework that provides a mathematical abstraction for diverse types of time series.<n>Fire consistently outperforms state-of-the-art models on long-term forecasting benchmarks.
  arXiv  Detail & Related papers  (2025-10-11T09:59:25Z)
• MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecasting [51.94256702463408]
  Time series predictability is derived from periodic characteristics at different frequencies.<n>We propose a novel time series forecasting method based on multi-frequency reference series correlation analysis.<n> Experiments on major open and synthetic datasets show state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-11T11:40:14Z)
• MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series [54.91026286579748]
  We propose a Multi-Grained Correlations-based Prediction Network. It simultaneously considers correlations at three levels to enhance prediction performance. It employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level.
  arXiv  Detail & Related papers  (2024-05-30T03:32:44Z)
• Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves [69.9104427437916]
  Multi-generator Wave Energy Converters (WEC) must handle multiple simultaneous waves coming from different directions called spread waves. These complex devices need controllers with multiple objectives of energy capture efficiency, reduction of structural stress to limit maintenance, and proactive protection against high waves. In this paper, we explore different function approximations for the policy and critic networks in modeling the sequential nature of the system dynamics.
  arXiv  Detail & Related papers  (2024-04-17T02:04:10Z)
• MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction [6.980076213134384]
  MultiWave is a novel framework that enhances deep learning time series models by incorporating components that operate at the intrinsic frequencies of signals. We show that MultiWave consistently identifies critical features and their frequency components, thus providing valuable insights into the applications studied.
  arXiv  Detail & Related papers  (2023-06-16T20:07:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.