PAMNet: Cycle-aware Phase-Amplitude Modulation Network for Multivariate Time Series Forecasting

• URL: http://arxiv.org/abs/2605.02938v1
• Date: Fri, 01 May 2026 06:59:59 GMT
• Title: PAMNet: Cycle-aware Phase-Amplitude Modulation Network for Multivariate Time Series Forecasting
• Abstract summary: We propose a Cycle-aware Phase-Amplitude Modulation Network (PAMNet) that explicitly decomposes periodic patterns into complementary phase and amplitude components.<n>Our method achieves state-of-the-art performance through its novel phase-amplitude decoupling mechanism.
• Score: 42.86661738011748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable periodic patterns serve as a fundamental basis for accurate multivariate time series forecasting. However, existing methods either implicitly extract periodicity through complex model architectures (e.g., Transformers) with high computational overhead or overlook the intrinsic phase-amplitude coupling when modeling periodic components explicitly. To address these issues, we propose a novel Cycle-aware Phase-Amplitude Modulation Network (PAMNet) that explicitly decomposes periodic patterns into complementary phase and amplitude components. The core innovation lies in its dual-branch modulator, featuring dedicated learnable embeddings for phase positioning and amplitude modulation. The phase branch employs cyclical embeddings to capture phase-dependent mean shifts, while the amplitude branch models intensity variations to adapt to changes in variance. A lightweight modulator with element-wise fusion efficiently combines these components, enabling explicit modeling of their interactions without complex attention mechanisms. Extensive experiments on twelve real-world datasets demonstrate that our method achieves state-of-the-art performance through its novel phase-amplitude decoupling mechanism, offering a new perspective for cyclical modeling in time series forecasting.

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