aiqbench analysis
SRT introduces a novel framework using disentangled rectified flow to reconstruct high-resolution time series from low-resolution inputs, effectively addressing data acquisition constraints in analytics. By decomposing signals into trend and seasonal components with a cross-resolution attention mechanism, the method achieves robust zero-shot super-resolution that outperforms existing approaches across nine public datasets. This work represents a significant step in adapting computer vision techniques to temporal data, offering a scalable solution for fine-grained signal reconstruction.
SRT: Super-Resolution for Time Series via Disentangled Rectified Flow
arXiv:2606.07605v1 Announce Type: new Abstract: Fine-grained time series data with high temporal resolution is critical for accurate analytics across a wide range of applications. However, the acquisition of such data is often limited by cost and feasibility. This problem can be tackled by reconstructing high-resolution signals from low-resolution inputs based on specific priors, known as super-resolution. While extensively studied in computer vision, directly transferring image super-resolution techniques to time series is not trivial. To address this challenge at a fundamental level, we propose Super-Resolution for Time series (SRT), a novel framework that reconstructs temporal patterns lost in low-resolution inputs via disentangled rectified flow. SRT decomposes the input into trend and seasonal components, aligns them to the target resolution using an implicit neural representation, and leverages a novel cross-resolution attention mechanism to guide the generation of high-resolution details. We further introduce SRT-large, a scaled-up version with extensive pre-training, which enables strong zero-shot super-resolution capability. Extensive experiments on nine public datasets demonstrate that SRT and SRT-large consistently outperform existing methods across multiple scale factors, showing both robust performance and the effectiveness of each component in our architecture.
Abstract:Fine-grained time series data with high temporal resolution is critical for accurate analytics across a wide range of applications. However, the acquisition of such data is often limited by cost and feasibility. This problem can be tackled by reconstructing high-resolution signals from low-resolution inputs based on specific priors, known as super-resolution. While extensively studied in computer vision, directly transferring image super-resolution techniques to time series is not trivial. To address this challenge at a fundamental level, we propose Super-Resolution for Time series (SRT), a novel framework that reconstructs temporal patterns lost in low-resolution inputs via disentangled rectified flow. SRT decomposes the input into trend and seasonal components, aligns them to the target resolution using an implicit neural representation, and leverages a novel cross-resolution attention mechanism to guide the generation of high-resolution details. We further introduce SRT-large, a scaled-up version with extensive pre-training, which enables strong zero-shot super-resolution capability. Extensive experiments on nine public datasets demonstrate that SRT and SRT-large consistently outperform existing methods across multiple scale factors, showing both robust performance and the effectiveness of each component in our architecture.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.07605 [cs.LG]
(or arXiv:2606.07605v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.07605 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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