Jun Wang
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- Stock Market Forecasting Methods 26
- Economics and Econometrics top 0.5%
- Complex Systems and Time Series Analysis 107
- Market Dynamics and Volatility 31
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- Chaos control and synchronization 57
- Finance top 2%
- Financial Risk and Volatility Modeling 34
- Polymers and Plastics top 5%
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- Energy Load and Power Forecasting 18
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- Advanced Sensor and Energy Harvesting Materials 15
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- Nonlinear Dynamics and Pattern Formation 14
- Co-authors
- Hongli NiuFang WenJie WangAniruddha PatilLiyun MaRonghui WuJunhuan ZhangYifan Zhang
- Cited by
- Management Science and Operations ResearchEconomics and EconometricsStatistical and Nonlinear Physics
- Journals
- Physica A Statistical Mechanics and its Applications (27 papers)Energy (8 papers)Nonlinear Dynamics (8 papers)
- Partner nations
- ChinaUnited StatesSingapore
In The Last Decade
Jun Wang
235 papers receiving 4.3k citations
Hit Papers
Peers
Comparison fields: 5 of 180
- Management Science and Operations Research 811
- Economics and Econometrics 1.8k
- Statistical and Nonlinear Physics 696
- Finance 490
- Polymers and Plastics 510
Countries citing papers authored by Jun Wang
This map shows the geographic impact of Jun Wang'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 Jun Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Wang more than expected).
Fields of papers citing papers by Jun Wang
This network shows the impact of papers produced by Jun Wang. 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 Jun Wang. The network helps show where Jun Wang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jun Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 4 | |
| 2 | 2024 | 4 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 6 | |
| 5 | 2024 | 9 | |
| 6 | 2024 | 3 | |
| 7 | 2024 | 1 | |
| 8 | 2024 | 6 | |
| 9 | 2024 | 1 | |
| 10 | 2024 | 2 | |
| 11 | 2024 | 6 | |
| 12 | 2024 | 1 | |
| 13 | 2024 | 4 | |
| 14 | 2024 | 7 | |
| 15 | 2024 | 4 | |
| 16 | 2024 | 0 | |
| 17 | 2023 | 60 | |
| 18 | 2023 | 39 | |
| 19 | Wearable and flexible electrochemical sensors for sweat analysis: a reviewbreakdown → | 2023 | 424 |
| 20 | 2010 | 4 |
About Jun Wang
Jun Wang is a scholar working on Statistical and Nonlinear Physics, Economics and Econometrics and Finance, having authored 258 papers that have together received 4.4k indexed citations. Recurring topics across this work include Complex Systems and Time Series Analysis (107 papers), Chaos control and synchronization (57 papers), Financial Risk and Volatility Modeling (34 papers), Market Dynamics and Volatility (31 papers), Stock Market Forecasting Methods (26 papers), Energy Load and Power Forecasting (18 papers), Advanced Sensor and Energy Harvesting Materials (15 papers) and Nonlinear Dynamics and Pattern Formation (14 papers). The work is most often cited by research in Management Science and Operations Research (811 citations), Economics and Econometrics (1.8k citations) and Statistical and Nonlinear Physics (696 citations). Jun Wang has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Hongli Niu, Fang Wen, Jie Wang, Aniruddha Patil, Liyun Ma, Ronghui Wu, Junhuan Zhang, Yifan Zhang, Lili Huang and Shuihong Zhu. Their work appears in journals such as Physica A Statistical Mechanics and its Applications, Energy, Nonlinear Dynamics, Physics Letters A and International Journal of Modern Physics C.
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.