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

2026-05-01

AI Summary

This work addresses the challenge of efficiently modeling periodicity in multivariate time series forecasting, where existing methods often fail to explicitly capture periodic patterns and neglect the intrinsic coupling between phase and amplitude. To overcome these limitations, the authors propose a lightweight, period-aware phase-amplitude modulation network that explicitly decouples and interactively models phase shifts and amplitude variations through a dual-branch architecture. The approach introduces learnable cyclic phase embeddings and element-wise amplitude modulation mechanisms, thereby avoiding computationally expensive attention structures. Extensive experiments on twelve real-world datasets demonstrate state-of-the-art performance, validating the effectiveness and superiority of explicitly disentangling phase and amplitude for periodic modeling in time series forecasting.

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.

Problem

• multivariate time series forecasting
• periodicity
• phase-amplitude coupling
• cycle-aware modeling

Innovation

• phase-amplitude modulation
• cycle-aware modeling
• time series forecasting
• dual-branch modulator
• periodicity decomposition