AdaDyTS: Dynamic Multi-Scale Spectral Decoupling and Time-Variant Inference for Time Series Forecasting

Abstract

Time series forecasting is fundamental to intelligent decision-making systems, enabling proactive planning and resource optimization across diverse application domains. However, the inherent complexity of real-world time series—including multi-scale temporal patterns, heterogeneous variable dependencies, and dynamic non-stationarity—poses significant challenges for existing forecasting models. Current approaches often suffer from high-frequency information attenuation in frequency-domain modeling, inadequate characterization of scale heterogeneity across variables, and limited capability to capture time-varying dynamics. To address these challenges, this paper introduces AdaDyTS, a unified knowledge-driven forecasting framework that synergistically integrates three complementary mechanisms: multi-scale frequency-domain interpolation decoupling via the Cascaded Spectral Residual Extractor (CSRE), dynamic morphological perception via the Dynamic Morphological Perception Unit (DMP-U), and time-variant state-space inference via the Time-Variant State-Space Module (TV-SS). CSRE separates low-frequency trends from high-frequency residuals through coarse-to-fine layer-wise self-reconstruction, preserving transient information that static filters typically attenuate. DMP-U employs deformable convolution guided by multi-expert attention to adaptively adjust receptive fields, enabling fine-grained modeling of local fluctuations and nonlinear distortions. TV-SS relaxes the conventional time-invariant parameter assumption, dynamically modulating state transition parameters to capture both short-term variations and long-term dependencies. Under a unified evaluation protocol across 13 benchmark datasets, AdaDyTS achieves average improvements of 4.35\% in MSE and 4.31\% in MAE over the AMD backbone, consistently outperforming state-of-the-art methods across long-horizon forecasting scenarios. The proposed framework demonstrates the effectiveness of integrating domain-specific knowledge—including spectral analysis, morphological feature extraction, and dynamic system modeling—within a unified deep learning architecture for enhanced predictive performance.