Chengqing Yu

1.8k total citations · 1 hit paper
43 papers, 1.0k citations indexed

About

Chengqing Yu is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Environmental Engineering. According to data from OpenAlex, Chengqing Yu has authored 43 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Electrical and Electronic Engineering, 12 papers in Artificial Intelligence and 10 papers in Environmental Engineering. Recurrent topics in Chengqing Yu's work include Energy Load and Power Forecasting (13 papers), Air Quality Monitoring and Forecasting (9 papers) and Traffic Prediction and Management Techniques (9 papers). Chengqing Yu is often cited by papers focused on Energy Load and Power Forecasting (13 papers), Air Quality Monitoring and Forecasting (9 papers) and Traffic Prediction and Management Techniques (9 papers). Chengqing Yu collaborates with scholars based in China, Australia and Denmark. Chengqing Yu's co-authors include Guangxi Yan, Xiwei Mi, Chengming Yu, Hui Liu, Haiping Wu, Zhu Duan, Xinwei Liu, Yu Bai, Zezhi Shao and Yongjun Xu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Cleaner Production and Energy.

In The Last Decade

Chengqing Yu

41 papers receiving 995 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis

2024 61 citations Zezhi Shao, Fei Wang et al. IEEE Transactions on Knowledge and Data Engineering profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Chengqing Yu China	19	447	295	202	156	155	43	1.0k
Guangxi Yan China	13	318 0.7×	171 0.6×	149 0.7×	77 0.5×	88 0.6×	19	630
Yiliao Song Australia	17	481 1.1×	494 1.7×	171 0.8×	104 0.7×	212 1.4×	32	1.1k
Paulo S. G. de Mattos Neto Brazil	17	362 0.8×	261 0.9×	182 0.9×	45 0.3×	294 1.9×	58	847
Hongmin Li China	18	601 1.3×	265 0.9×	269 1.3×	50 0.3×	260 1.7×	33	1.1k
Zhu Duan China	24	962 2.2×	430 1.5×	553 2.7×	89 0.6×	261 1.7×	49	1.7k
Jiazheng Li China	7	311 0.7×	179 0.6×	172 0.9×	53 0.3×	450 2.9×	12	971
Xiwei Mi China	18	1.7k 3.8×	751 2.5×	349 1.7×	185 1.2×	351 2.3×	29	2.2k
Kequan Zhang China	16	955 2.1×	379 1.3×	179 0.9×	62 0.4×	322 2.1×	34	1.4k
J. F. Torres Spain	11	445 1.0×	316 1.1×	148 0.7×	131 0.8×	234 1.5×	22	1.1k

Countries citing papers authored by Chengqing Yu

Since Specialization

Citations

This map shows the geographic impact of Chengqing Yu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Chengqing Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chengqing Yu more than expected).

Fields of papers citing papers by Chengqing Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chengqing Yu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Chengqing Yu. The network helps show where Chengqing Yu may publish in the future.

Co-authorship network of co-authors of Chengqing Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Chengqing Yu. A scholar is included among the top collaborators of Chengqing Yu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Chengqing Yu. Chengqing Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Yu, Chengqing, Fei Wang, Zezhi Shao, et al.. (2025). GinAR+: A Robust End-to-End Framework for Multivariate Time Series Forecasting With Missing Values. IEEE Transactions on Knowledge and Data Engineering. 37(8). 4635–4648. 5 indexed citations

Yu, Chengqing, Fei Wang, Chuanguang Yang, et al.. (2025). Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates. 3633–3644.

Yu, Fuhao, et al.. (2025). Distributed Traffic Signal Control Model for Accurate Policy Learning Under Dynamic Traffic Flow: A Graph Forecast-State Vector Driven Deep Reinforcement Learning Framework. IEEE Transactions on Intelligent Transportation Systems. 26(9). 13573–13584. 1 indexed citations

Shao, Zezhi, Tao Sun, Chengqing Yu, et al.. (2025). HUTFormer: Hierarchical U-Net transformer for long-term traffic forecasting. SHILAP Revista de lepidopterología. 5. 100218–100218.

Yu, Chengqing, Fei Wang, Yilun Wang, et al.. (2024). MGSFformer: A Multi-Granularity Spatiotemporal Fusion Transformer for air quality prediction. Information Fusion. 113. 102607–102607. 26 indexed citations

Shao, Zezhi, Fei Wang, Yongjun Xu, et al.. (2024). Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis. IEEE Transactions on Knowledge and Data Engineering. 37(1). 291–305. 61 indexed citations breakdown →

Yu, Chengqing, Fei Wang, Zezhi Shao, et al.. (2024). GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing. 3989–4000. 19 indexed citations

Chen, Chao, et al.. (2024). TFEformer: A new temporal frequency ensemble transformer for day-ahead photovoltaic power prediction. Journal of Cleaner Production. 448. 141690–141690. 21 indexed citations

Yu, Chengqing, et al.. (2024). MRIformer: A multi-resolution interactive transformer for wind speed multi-step prediction. Information Sciences. 661. 120150–120150. 14 indexed citations

10.

Yu, Chengqing, Jian Yang, Haoran Li, et al.. (2024). Prognostic prediction and treatment options for gastric signet ring cell carcinoma: a SEER database analysis. Frontiers in Oncology. 14. 1473798–1473798. 1 indexed citations

11.

Shi, Ziyi, et al.. (2024). WGformer: A Weibull-Gaussian Informer based model for wind speed prediction. Engineering Applications of Artificial Intelligence. 131. 107891–107891. 24 indexed citations

12.

Liu, Hui, et al.. (2023). A novel AQI forecasting method based on fusing temporal correlation forecasting with spatial correlation forecasting. Atmospheric Pollution Research. 14(4). 101717–101717. 17 indexed citations

13.

Yu, Chengqing, et al.. (2023). An ensemble convolutional reinforcement learning gate network for metro station PM2.5 forecasting. Stochastic Environmental Research and Risk Assessment. 39(10). 4195–4210. 8 indexed citations

14.

Yu, Chengqing, Guangxi Yan, Chengming Yu, & Xiwei Mi. (2023). Attention mechanism is useful in spatio-temporal wind speed prediction: Evidence from China. Applied Soft Computing. 148. 110864–110864. 32 indexed citations

15.

Jin, Weiqiu, Shuqing Dong, Chengqing Yu, & Qingquan Luo. (2022). A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning. Computers in Biology and Medicine. 146. 105560–105560. 31 indexed citations

16.

Yang, Jian, Jie Wang, Chengqing Yu, et al.. (2022). Development and validation of a tissue-based DNA methylation risk-score model to predict the prognosis of surgically resected pancreatic cancer patients. Gland Surgery. 11(10). 1697–1711. 3 indexed citations

17.

Mi, Xiwei, et al.. (2022). A dynamic ensemble deep deterministic policy gradient recursive network for spatiotemporal traffic speed forecasting in an urban road network. Digital Signal Processing. 129. 103643–103643. 19 indexed citations

18.

Liu, Hui, et al.. (2022). A new PM2.5 forecasting model based on data preprocessing, reinforcement learning and gated recurrent unit network. Atmospheric Pollution Research. 13(7). 101475–101475. 16 indexed citations

19.

Yan, Guangxi, Yu Bai, Chengqing Yu, & Chengming Yu. (2022). A Multi-Factor Driven Model for Locomotive Axle Temperature Prediction Based on Multi-Stage Feature Engineering and Deep Learning Framework. Machines. 10(9). 759–759. 7 indexed citations

20.

Huang, Li, et al.. (2022). Wind-speed prediction model based on variational mode decomposition, temporal convolutional network, and sequential triplet loss. Sustainable Energy Technologies and Assessments. 52. 101980–101980. 26 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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