WaveStitch: Flexible and Fast Conditional Time Series Generation With Diffusion Models
Summary: WaveStitch: diffusion model with dual-source conditioning on metadata and partially observed signals, using a hybrid train/inference design (metadata in training, gradient-based guidance on observations at inference). Parallel window generation with stitching preserves coherence, giving ~1.8× lower MSE and up to 166× faster sampling vs autoregressive SOTA. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Aditya Shankar
- 2. Lydia Chen
- 3. Arie van Deursen
- 4. Rihan Hai
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,627 | Data Cleaning: Overview and Emerging Challenges | 2016 | SIGMOD | 0.00011086905 |
| 2,298 | TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods | 2024 | VLDB | 9.0742746e-05 |
| 2,421 | Data Synthesis based on Generative Adversarial Networks | 2018 | VLDB | 8.8514021e-05 |
| 4,888 | Forecasting Big Time Series: Old and New | 2018 | VLDB | 5.8531064e-05 |
| 5,777 | ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection | 2024 | VLDB | 5.3308813e-05 |
| 8,224 | TSGBench: Time Series Generation Benchmark | 2024 | VLDB | 4.5552948e-05 |
| 8,523 | Controllable Tabular Data Synthesis Using Diffusion Models | 2024 | SIGMOD | 4.4937074e-05 |
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