DrafTS: Time-Aware Decomposition with Residual Correction for Time Series Modeling

2026-09-27

AI Summary

This study addresses the challenge of pattern degradation in time series caused by noise-dynamics coupling, where conventional filtering tends to suppress meaningful temporal dynamics. To overcome this, we propose a model-agnostic framework for time-aware decomposition and residual correction. By leveraging instantaneous amplitude and frequency characteristics to guide signal decomposition, the method isolates principal components for modeling while employing a lightweight module to correct residuals, thereby preserving evolutionary dynamics during denoising. This framework can be seamlessly integrated into diverse backbone architectures. Extensive experiments across six model types and four time series tasks demonstrate consistent and significant performance improvements, validating its strong generalizability and effectiveness.

Abstract

Real-world time series contain evolving underlying dynamics with irregular variations that lack stable temporal patterns and are often referred to as noise. Existing methods address this mixture by filtering frequencies or suppressing noisy observations. They either miss temporal evolution or risk suppressing useful dynamics. We propose DrafTS, a model-agnostic framework that aims to reduce noise while preserving evolving dynamics through time-aware Decomposition with ResiduAl correction For Time Series. DrafTS uses features derived from instantaneous amplitude and frequency to guide decomposition into a primary component intended to capture underlying dynamics. A task-specific backbone models the primary component, while a lightweight correction module uses residual information to correct the backbone output. Across four time series modeling tasks, DrafTS improves six diverse backbones, demonstrating its effectiveness. Code is at https://github.com/Autumn61q/DrafTS

Problem

• Time Series Modeling
• Noise Reduction
• Evolving Dynamics
• Time-Aware Decomposition

Innovation

• Time-Aware Decomposition
• Residual Correction
• Model-Agnostic Framework
• Instantaneous Amplitude and Frequency
• Time Series Modeling