WaveTuner: Comprehensive Wavelet Subband Tuning for Time Series Forecasting

2025-11-24

AI Summary

Existing wavelet-based time series forecasting methods over-rely on low-frequency components while neglecting the critical role of high-frequency details in prediction accuracy. To address this, we propose WaveTuner—a novel framework enabling full-spectrum subband co-optimization in the wavelet domain. It introduces an adaptive wavelet refinement module to generate time-frequency coefficients and a dynamic routing mechanism to learn subband-specific weights. Furthermore, we design a multi-branch, specialized network based on the Kolmogorov–Arnold Network (KAN) to model frequency-band-specific features, jointly capturing global trends and local dynamics. Extensive experiments across eight real-world datasets demonstrate that WaveTuner significantly enhances multi-scale modeling capability and achieves state-of-the-art (SOTA) performance in long-term forecasting tasks.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Due to the inherent complexity, temporal patterns in real-world time series often evolve across multiple intertwined scales, including long-term periodicity, short-term fluctuations, and abrupt regime shifts. While existing literature has designed many sophisticated decomposition approaches based on the time or frequency domain to partition trend-seasonality components and high-low frequency components, an alternative line of approaches based on the wavelet domain has been proposed to provide a unified multi-resolution representation with precise time-frequency localization. However, most wavelet-based methods suffer from a persistent bias toward recursively decomposing only low-frequency components, severely underutilizing subtle yet informative high-frequency components that are pivotal for precise time series forecasting. To address this problem, we propose WaveTuner, a Wavelet decomposition framework empowered by full-spectrum subband Tuning for time series forecasting. Concretely, WaveTuner comprises two key modules: (i) Adaptive Wavelet Refinement module, that transforms time series into time-frequency coefficients, utilizes an adaptive router to dynamically assign subband weights, and generates subband-specific embeddings to support refinement; and (ii) Multi-Branch Specialization 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. Equipped with these modules, WaveTuner comprehensively tunes global trends and local variations within a unified time-frequency framework. Extensive experiments on eight real-world datasets demonstrate WaveTuner achieves state-of-the-art forecasting performance in time series forecasting.

Problem

• Existing wavelet methods neglect informative high-frequency components
• Current approaches fail to fully utilize multi-resolution time-frequency representations
• Time series forecasting lacks comprehensive tuning of global and local patterns

Innovation

• Adaptive wavelet refinement with dynamic subband weighting
• Multi-branch specialization using Kolmogorov-Arnold Networks
• Full-spectrum tuning of global trends and local variations