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
- 2. Dalin Zhang
- 3. Miao Zhang
- 4. Chenjuan Guo
- 5. Bin Yang
- 6. Christian S. Jensen
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 |
|---|---|---|---|---|
| 1,992 | Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning | 2020 | SIGMOD | 9.8415851e-05 |
| 2,388 | Anytime Stochastic Routing with Hybrid Learning | 2020 | VLDB | 8.9047008e-05 |
| 2,845 | VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition | 2021 | VLDB | 8.0301674e-05 |
| 3,191 | AutoAI-TS: AutoAI for Time Series Forecasting | 2021 | SIGMOD | 7.4126641e-05 |
| 4,476 | Classical and Contemporary Approaches to Big Time Series Forecasting | 2019 | SIGMOD | 6.1458665e-05 |
| 5,023 | AutoCTS: Automated Correlated Time Series Forecasting | 2022 | VLDB | 5.7473072e-05 |
| 6,139 | Finding Label and Model Errors in Perception Data With Learned Observation Assertions | 2022 | SIGMOD | 5.1893488e-05 |
| 11,202 | LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation | 2023 | SIGMOD | 4.1905499e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,206 | Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting | 2022 | VLDB | 6.3534357e-05 |
| 11,147 | Weakly Guided Adaptation for Robust Time Series Forecasting | 2024 | VLDB | 4.1905499e-05 |
| 6,419 | AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data | 2024 | VLDB | 5.0621949e-05 |
| 4,476 | Classical and Contemporary Approaches to Big Time Series Forecasting | 2019 | SIGMOD | 6.1458665e-05 |
| 13,129 | A Memory Guided Transformer for Time Series Forecasting | 2025 | VLDB | - |
| 5,449 | Multiple Time Series Forecasting with Dynamic Graph Modeling | 2024 | VLDB | 5.4980173e-05 |
| 3,191 | AutoAI-TS: AutoAI for Time Series Forecasting | 2021 | SIGMOD | 7.4126641e-05 |
| 9,329 | LightCTS: A Lightweight Framework for Correlated Time Series Forecasting | 2023 | SIGMOD | 4.351469e-05 |
| 13,127 | Fully Automated Correlated Time Series Forecasting in Minutes | 2025 | VLDB | - |
| 5,023 | AutoCTS: Automated Correlated Time Series Forecasting | 2022 | VLDB | 5.7473072e-05 |