AmpTS: Amplitude-Guided Progressive Restoration Method for Time-Series Forecasting

Time-series forecasting (TSF) plays an important role in a wide range of real-world applications. Existing TSF methods typically either generate the complete future sequence through a single direct mapping or formulate forecasting as a noise-conditioned generative task that progressively recovers the future through multiple denoising stages. Although the latter provides a progressive forecasting perspective, diffusion-based approaches generally rely on stochastic perturbations to construct intermediate states, with their progressive processes constrained by predefined noise-evolution paths. To address this limitation, we propose AmpTS, an amplitude-guided progressive restoration method that uses structure-preserving reference amplitude levels to organize iterative transitions among self-generated restoration states. Starting from a zero state, AmpTS repeatedly re-estimates the complete future sequence at each restoration stage by conditioning on historical observations and the current state, enabling multi-stage progressive forecasting without relying on stochastic perturbations to construct restoration states. Extensive experiments on real-world datasets show that AmpTS achieves competitive and consistent performance against representative direct forecasting and diffusion-based forecasting methods, demonstrating the effectiveness of amplitude-guided progressive restoration for time-series point forecasting.