SRT: New Framework for High-Resolution Time Series Reconstruction Using Disentangled Rectified Flow
A research team has introduced Super-Resolution for Time series (SRT), a framework that reconstructs high-resolution temporal data from low-resolution inputs using a disentangled rectified flow approach. The work, accepted to ICLR 2026, addresses the challenge that image super-resolution techniques do not transfer directly to time series data. The method has implications for any domain where fine-grained temporal data is costly or difficult to acquire.
The SRT framework tackles the problem of time series super-resolution by decomposing input signals into trend and seasonal components, aligning them to a target resolution via implicit neural representation, and applying a novel cross-resolution attention mechanism to recover high-resolution detail. The authors also introduce SRT-large, a scaled-up variant pre-trained extensively to enable zero-shot super-resolution — meaning it can generalize to new datasets without task-specific training. Experiments across nine public datasets show SRT and SRT-large consistently outperform existing methods at multiple upscaling factors. The paper was accepted to the Fourteenth International Conference on Learning Representations (ICLR 2026), a top-tier machine learning venue. The work highlights a growing recognition that temporal data modalities require domain-specific generative modeling approaches rather than direct adaptation from computer vision.
What's missing
The paper does not specify which nine public datasets were used for evaluation, nor does it detail the computational cost or hardware requirements for training SRT-large. Limitations around the types of time series (e.g., irregularly sampled, multivariate with many channels) where the method may underperform are not discussed in the abstract. The degree of improvement over baselines and whether gains are statistically significant are also not reported here.
What different sources said
- arXiv cs.AICenter
SRT: Super-Resolution for Time Series via Disentangled Rectified Flow
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