aiqbench analysis
DrafTS introduces a model-agnostic framework that separates evolving dynamics from noise in time series using instantaneous amplitude and frequency features, then applies residual correction to refine predictions. This approach enhances six diverse backbones across four tasks without relying on specific filtering techniques that might discard useful signals. The method represents a significant advancement in handling irregular temporal patterns for machine learning applications.
DrafTS: Time-Aware Decomposition with Residual Correction for Time Series Modeling
arXiv:2609.33368v1 Announce Type: new 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
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 this https URL
Artificial Intelligence (cs.AI)
arXiv:2609.33368 [cs.AI]
(or arXiv:2609.33368v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.33368 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 223 words
