Computer Science > Machine Learning
arXiv:2603.09661 (cs)
[Submitted on 10 Mar 2026]
Title:FreqCycle: A Multi-Scale Time-Frequency Analysis Method for Time Series Forecasting
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Abstract:Mining time-frequency features is critical for time series forecasting. Existing research has predominantly focused on modeling low-frequency patterns, where most time series energy is concentrated. The overlooking of mid to high frequency continues to limit further performance gains in deep learning models. We propose FreqCycle, a novel framework integrating: (i) a Filter-Enhanced Cycle Forecasting (FECF) module to extract low-frequency features by explicitly learning shared periodic patterns in the time domain, and (ii) a Segmented Frequency-domain Pattern Learning (SFPL) module to enhance mid to high frequency energy proportion via learnable filters and adaptive weighting. Furthermore, time series data often exhibit coupled multi-periodicity, such as intertwined weekly and daily cycles. To address coupled multi-periodicity as well as long lookback window challenges, we extend FreqCycle hierarchically into MFreqCycle, which decouples nested periodic features through cross-scale interactions. Extensive experiments on seven diverse domain benchmarks demonstrate that FreqCycle achieves state-of-the-art accuracy while maintaining faster inference speeds, striking an optimal balance between performance and efficiency.
| Comments: | |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.09661 [cs.LG] |
| (or arXiv:2603.09661v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09661
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View a PDF of the paper titled FreqCycle: A Multi-Scale Time-Frequency Analysis Method for Time Series Forecasting, by Boya Zhang and 3 other authors
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