TSGBench: Time Series Generation Benchmark
Summary: TSGBench: first unified benchmark for synthetic time-series generation, with curated real-world datasets and standardized preprocessing plus a broad evaluation suite (vanilla and distance-based measures). Adds a domain-adaptation generalization test and statistical rankings revealing method variability across datasets. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yihao Ang (National University of Singapore)
- 2. Qiang Huang (National University of Singapore)
- 3. Yifan Bao (National University of Singapore)
- 4. Anthony K. H. Tung (National University of Singapore)
- 5. Zhiyong Huang (National University of Singapore)
BibTeX Citation
@article{ang_vldb24,
title = {{TSGBench: Time Series Generation Benchmark}},
author = {Ang, Yihao and Huang, Qiang and Bao, Yifan and Tung, Anthony K. H. and Huang, Zhiyong},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {3},
pages = {305--318},
doi = {10.14778/3632093.3632097},
url = {https://doi.org/10.14778/3632093.3632097},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,871 | TSGAssist: An Interactive Assistant Harnessing LLMs and RAG for Time Series Generation Recommendations and Benchmarking | 2024 | VLDB | 5.5271792e-05 |
| 10,416 | WaveStitch: Flexible and Fast Conditional Time Series Generation With Diffusion Models | 2026 | SIGMOD | 5.093636e-05 |
| 10,923 | Improving Time Series Data Compression in Apache IoTDB | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,534 | Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series | 2021 | VLDB | 0.00010464308 |
| 2,004 | TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection | 2022 | VLDB | 9.3207067e-05 |
| 3,447 | Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection | 2022 | VLDB | 7.4085215e-05 |
| 4,851 | Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles | 2022 | VLDB | 6.4803317e-05 |
| 5,129 | AutoCTS: Automated Correlated Time Series Forecasting | 2022 | VLDB | 6.3548172e-05 |
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