TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
Summary: TFB is an automated, bias-aware TSF benchmark spanning 10 domains and flexible pipelines for statistical, ML, and deep methods. Evaluations across 8,068 univariate series and 25 multivariate datasets reveal method suitability across datasets and settings. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiangfei Qiu (East China Normal University)
- 2. Jilin Hu (East China Normal University)
- 3. Lekui Zhou (Huawei)
- 4. Xingjian Wu (East China Normal University)
- 5. Junyang Du (East China Normal University)
- 6. Buang Zhang (East China Normal University)
- 7. Chenjuan Guo (East China Normal University)
- 8. Aoying Zhou (East China Normal University)
- 9. Christian S. Jensen (Aalborg University)
- 10. Zhenli Sheng (Huawei)
- 11. Bin Yang (East China Normal University)
BibTeX Citation
@article{qiu_vldb24,
title = {{TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods}},
author = {Qiu, Xiangfei and Hu, Jilin and Zhou, Lekui and Wu, Xingjian and Du, Junyang and Zhang, Buang and Guo, Chenjuan and Zhou, Aoying and Jensen, Christian S. and Sheng, Zhenli and Yang, Bin},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {9},
pages = {2363--2377},
doi = {10.14778/3665844.3665863},
url = {https://doi.org/10.14778/3665844.3665863},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 11 of 11 citing papers.
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 5,129 | AutoCTS: Automated Correlated Time Series Forecasting | 2022 | VLDB |
| 2 | 10,977 | TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection | 2025 | VLDB |
| 3 | 5,498 | SimpleTS: An Efficient and Universal Model Selection Framework for Time Series Forecasting | 2023 | VLDB |
| 4 | 5,158 | Forecasting Big Time Series: Old and New | 2018 | VLDB |
| 5 | 11,350 | Weakly Guided Adaptation for Robust Time Series Forecasting | 2024 | VLDB |
| 6 | 6,078 | Multiple Time Series Forecasting with Dynamic Graph Modeling | 2024 | VLDB |
| 7 | 4,424 | Classical and Contemporary Approaches to Big Time Series Forecasting | 2019 | SIGMOD |
| 8 | 9,382 | TAB: Unified Benchmarking of Time Series Anomaly Detection Methods | 2025 | VLDB |
| 9 | 13,318 | Fully Automated Correlated Time Series Forecasting in Minutes | 2025 | VLDB |
| 10 | 4,141 | AutoAI-TS: AutoAI for Time Series Forecasting | 2021 | SIGMOD |