King Ma
Impact in
- Signal Processing top 10%
- Time Series Analysis and Forecasting
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- Stock Market Forecasting Methods
- Forecasting Techniques and Applications
Papers in
-
- Anomaly Detection Techniques and Applications 3
- Sentiment Analysis and Opinion Mining 2
- Adversarial Robustness in Machine Learning 1
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- Time Series Analysis and Forecasting 4
- Co-authors
- Henry Leung (9 shared papers)Wei Wang (1 shared paper)Zhongyang Han (1 shared paper)Jun Zhao (1 shared paper)Daniel Q. Huang (2 shared papers)Henry Leung (1 shared paper)
- Journals
- Chaos Solitons & Fractals (1 paper)IEEE Sensors Journal (1 paper)IEEE Sensors Letters (1 paper)DOAJ (DOAJ: Directory of Open Access Journals) (1 paper)Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (1 paper)
- Partner nations
- CanadaBangladeshChina
In The Last Decade
King Ma
9 papers receiving 363 citations
King Ma's Hit Papers
Peers
Comparison fields: 5 of 93
- Signal Processing 72
- Management Science and Operations Research 57
- Artificial Intelligence 111
- Building and Construction 41
- Environmental Engineering 40
Countries citing papers authored by King Ma
This map shows the geographic impact of King Ma'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 King Ma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites King Ma more than expected).
Fields of papers citing papers by King Ma
This network shows the impact of papers produced by King Ma. 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 King Ma. The network helps show where King Ma may publish in the future.
Co-authors
The 6 scholars most cited alongside King Ma, 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 | A Review of Deep Learning Models for Time Series Prediction Hit paper breakdown → | 2019 | 333 |
| 2 | 2019 | 15 | |
| 3 | 2017 | 5 | |
| 4 | 2017 | 5 | |
| 5 | 2019 | 4 | |
| 6 | 2017 | 3 | |
| 7 | 2023 | 3 | |
| 8 | 2023 | 1 | |
| 9 | 2023 | 1 | |
| 10 | 2024 | 0 | |
| 11 | 2024 | 0 |
About King Ma
King Ma is a scholar working on Artificial Intelligence, Signal Processing, Management Science and Operations Research, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 11 papers that have together received 370 indexed citations. Recurring topics across this work include Time Series Analysis and Forecasting (4 papers), Anomaly Detection Techniques and Applications (3 papers), Stock Market Forecasting Methods (3 papers), Sentiment Analysis and Opinion Mining (2 papers), Traffic Prediction and Management Techniques (2 papers), UAV Applications and Optimization (2 papers), Nonlinear Dynamics and Pattern Formation (1 paper) and Adversarial Robustness in Machine Learning (1 paper). The work is most often cited by research in Signal Processing (72 citations), Management Science and Operations Research (57 citations), Artificial Intelligence (111 citations), Building and Construction (41 citations) and Environmental Engineering (40 citations). King Ma has collaborated with scholars based in Canada, Bangladesh and China. Frequent co-authors include Henry Leung, Wei Wang, Zhongyang Han, Jun Zhao, Daniel Q. Huang and Henry Leung. Their work appears in journals such as Chaos Solitons & Fractals, IEEE Sensors Journal, IEEE Sensors Letters, DOAJ (DOAJ: Directory of Open Access Journals) and Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE.
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.