Tag
This paper proposes SRT (Super-Resolution for Time Series), a framework that reconstructs high-resolution temporal patterns from low-resolution inputs using a disentangled rectified flow approach. The method decomposes input into trend and seasonal components, applies implicit neural representation for resolution alignment, and introduces cross-resolution attention to generate fine-grained details, achieving state-of-the-art performance on multiple datasets.