Paper
FreqCycle: A Multi-Scale Time-Frequency Analysis Method for Time Series Forecasting
Authors
Boya Zhang, Shuaijie Yin, Huiwen Zhu, Xing He
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.09661v1</id>\n <title>FreqCycle: A Multi-Scale Time-Frequency Analysis Method for Time Series Forecasting</title>\n <updated>2026-03-10T13:34:36Z</updated>\n <link href='https://arxiv.org/abs/2603.09661v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.09661v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n <published>2026-03-10T13:34:36Z</published>\n <arxiv:comment>18 pages, 17 figures, accepted to AAAI 2026. Code available at https://github.com/boya-zhang-ai/FreqCycle</arxiv:comment>\n <arxiv:primary_category term='cs.LG'/>\n <author>\n <name>Boya Zhang</name>\n </author>\n <author>\n <name>Shuaijie Yin</name>\n </author>\n <author>\n <name>Huiwen Zhu</name>\n </author>\n <author>\n <name>Xing He</name>\n </author>\n </entry>"
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