Chang Wei Tan
- Artificial Intelligence top 5%
- Signal Processing top 5%
- Electrical and Electronic Engineering
- Biomedical Engineering
- Computational Mechanics top 10%
- Co-authors
- Geoffrey I. WebbChristoph BergmeirMahsa SalehiNavid Mohammadi FoumaniFrançois PetitjeanEnhong ChenGermain ForestierLynn Miller
- Topics
- Time Series Analysis and Forecasting (16 papers)Music and Audio Processing (8 papers)Anomaly Detection Techniques and Applications (6 papers)
- Journals
- Journal of the American Chemical SocietyScientific ReportsInternational Journal of Heat and Mass Transfer
- Partner nations
- AustraliaChinaUnited States
In The Last Decade
Chang Wei Tan
46 papers receiving 771 citations
Hit Papers
Peers
Comparison fields: 5 of 118
- Artificial Intelligence 252
- Signal Processing 250
- Electrical and Electronic Engineering 95
- Biomedical Engineering 95
- Computational Mechanics 70
Countries citing papers authored by Chang Wei Tan
This map shows the geographic impact of Chang Wei Tan'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 Chang Wei Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chang Wei Tan more than expected).
Fields of papers citing papers by Chang Wei Tan
This network shows the impact of papers produced by Chang Wei Tan. 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 Chang Wei Tan. The network helps show where Chang Wei Tan may publish in the future.
Co-authorship network of co-authors of Chang Wei Tan
This figure shows the co-authorship network connecting the top 25 collaborators of Chang Wei Tan. A scholar is included among the top collaborators of Chang Wei Tan 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 Chang Wei Tan. Chang Wei Tan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | Deep Learning for Time Series Classification and Extrinsic Regression: A Current Surveybreakdown → | 80 |
| 6 | 1 | |
| 7 | 73 | |
| 8 | 8 | |
| 9 | 10 | |
| 10 | 17 | |
| 11 | 105 | |
| 12 | 3 | |
| 13 | 8 | |
| 14 | Monash University, UEA, UCR Time Series Regression Archive | 4 |
| 15 | 2 | |
| 16 | 23 | |
| 17 | 0 | |
| 18 | 3 | |
| 19 | Prediction for Mobile Application Usage Patterns | 22 |
| 20 | 1 |
About Chang Wei Tan
Chang Wei Tan is a scholar working on Signal Processing, Transportation and Computer Science Applications, having authored 51 papers that have together received 797 indexed citations. Recurring topics across this work include Time Series Analysis and Forecasting (16 papers), Music and Audio Processing (8 papers) and Anomaly Detection Techniques and Applications (6 papers). The work is most often cited by research in Signal Processing (250 citations), Transportation (65 citations) and Artificial Intelligence (252 citations). Chang Wei Tan has collaborated with scholars based in Australia, China and United States. Frequent co-authors include Geoffrey I. Webb, Christoph Bergmeir, Mahsa Salehi, Navid Mohammadi Foumani, François Petitjean, Enhong Chen, Germain Forestier, Lynn Miller, Hui Xiong and Qi Liu. Their work appears in journals such as Journal of the American Chemical Society, Scientific Reports and International Journal of Heat and Mass Transfer.
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.