AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting
Summary: AutoCTS+ optimizes architecture and hyperparameters for correlated CTS by encoding candidates as a joint graph. An Architecture-Hyperparameter Comparator (AHC) ranks candidates and enables scalable end-to-end selection, surpassing manual designs. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xinle Wu (Aalborg University)
- 2. Dalin Zhang (Aalborg University)
- 3. Miao Zhang (Aalborg University; Harbin Engineering University)
- 4. Chenjuan Guo (Aalborg University; East China Normal University)
- 5. Bin Yang (Aalborg University; East China Normal University)
- 6. Christian S. Jensen (Aalborg University)
BibTeX Citation
@inproceedings{wu_sigmod23,
title = {{AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting}},
author = {Wu, Xinle and Zhang, Dalin and Zhang, Miao and Guo, Chenjuan and Yang, Bin and Jensen, Christian S.},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588951},
url = {https://dl.acm.org/doi/10.1145/3588951},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 17 of 17 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,048 | Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning | 2020 | SIGMOD | 9.1229916e-05 |
| 3,331 | VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition | 2021 | VLDB | 7.4166095e-05 |
| 3,497 | Anytime Stochastic Routing with Hybrid Learning | 2020 | VLDB | 7.2556245e-05 |
| 4,185 | AutoAI-TS: AutoAI for Time Series Forecasting | 2021 | SIGMOD | 6.7504375e-05 |
| 4,520 | Classical and Contemporary Approaches to Big Time Series Forecasting | 2019 | SIGMOD | 6.5628257e-05 |
| 5,089 | AutoCTS: Automated Correlated Time Series Forecasting | 2022 | VLDB | 6.2800456e-05 |
| 6,552 | Finding Label and Model Errors in Perception Data With Learned Observation Assertions | 2022 | SIGMOD | 5.750057e-05 |
| 11,717 | LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation | 2023 | SIGMOD | 4.9793485e-05 |
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