PAMod models cyclical distribution shifts in non-stationary time series via phase-amplitude modulation in normalized space, proving equivalence to dynamic denormalization and achieving SOTA on twelve benchmarks.
Advances in Neural Information Processing Systems (NeurIPS) , pages =
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UNVERDICTED 2representative citing papers
PAMNet achieves state-of-the-art multivariate time series forecasting by explicitly separating and modulating the phase and amplitude of periodic cycles via a lightweight dual-branch network.
citing papers explorer
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PAMod: Modeling Cyclical Shifts via Phase-Amplitude Modulation for Non-stationary Time Series Forecasting
PAMod models cyclical distribution shifts in non-stationary time series via phase-amplitude modulation in normalized space, proving equivalence to dynamic denormalization and achieving SOTA on twelve benchmarks.
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PAMNet: Cycle-aware Phase-Amplitude Modulation Network for Multivariate Time Series Forecasting
PAMNet achieves state-of-the-art multivariate time series forecasting by explicitly separating and modulating the phase and amplitude of periodic cycles via a lightweight dual-branch network.