SRT: Super-Resolution for Time Series via Disentangled Rectified Flow
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 ...
| Authors | Jufang Duan, Shenglong Xiao, Yuren Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 29 May 2026 |
| Last updated at source | 9 June 2026 |
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