Chang Wei Tan

1.6k total citations · 1 hit paper
51 papers, 797 citations indexed

About

Chang Wei Tan is a scholar working on Signal Processing, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Chang Wei Tan has authored 51 papers receiving a total of 797 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Signal Processing, 9 papers in Electrical and Electronic Engineering and 7 papers in Artificial Intelligence. Recurrent topics in Chang Wei Tan's work include Time Series Analysis and Forecasting (16 papers), Music and Audio Processing (8 papers) and Anomaly Detection Techniques and Applications (6 papers). Chang Wei Tan is often cited by papers focused on Time Series Analysis and Forecasting (16 papers), Music and Audio Processing (8 papers) and Anomaly Detection Techniques and Applications (6 papers). Chang Wei Tan collaborates with scholars based in Australia, China and United States. Chang Wei Tan's co-authors include Geoffrey I. Webb, Christoph Bergmeir, Mahsa Salehi, Navid Mohammadi Foumani, François Petitjean, Enhong Chen, Germain Forestier, Hui Xiong, Lynn Miller and Qi Liu and has published in prestigious journals such as Journal of the American Chemical Society, Scientific Reports and International Journal of Heat and Mass Transfer.

In The Last Decade

Chang Wei Tan

46 papers receiving 771 citations

Hit Papers

Deep Learning for Time Series Classification and Extrinsi... 2024 2026 2025 2024 25 50 75

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chang Wei Tan Australia 15 252 250 95 95 70 51 797
Xifeng Guo China 13 862 3.4× 159 0.6× 230 2.4× 59 0.6× 36 0.5× 36 1.7k
Jing Cheng China 16 74 0.3× 47 0.2× 44 0.5× 135 1.4× 134 1.9× 120 1.1k
Jianpei Zhang China 19 548 2.2× 59 0.2× 137 1.4× 114 1.2× 13 0.2× 124 1.2k
Christophe García France 20 304 1.2× 234 0.9× 107 1.1× 75 0.8× 22 0.3× 83 1.9k
Wen-Sheng Chen China 19 196 0.8× 131 0.5× 73 0.8× 73 0.8× 102 1.5× 88 1.2k
Xinjing Wang China 19 476 1.9× 67 0.3× 112 1.2× 34 0.4× 46 0.7× 92 1.5k
Anuj Sharma India 13 227 0.9× 40 0.2× 63 0.7× 60 0.6× 21 0.3× 53 979
N. Davey United Kingdom 15 267 1.1× 121 0.5× 89 0.9× 64 0.7× 6 0.1× 54 779
Jeff Heaton United States 9 290 1.2× 93 0.4× 84 0.9× 53 0.6× 25 0.4× 16 849
Jilin Li China 31 1.1k 4.2× 577 2.3× 55 0.6× 174 1.8× 35 0.5× 71 3.4k

Countries citing papers authored by Chang Wei Tan

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Xiang, Yu, et al.. (2025). A clustering fractional-order grey model in short-term electrical load forecasting. Scientific Reports. 15(1). 6207–6207. 2 indexed citations
2.
Tan, Chang Wei, et al.. (2025). Proximity forest 2.0: a new effective and scalable similarity-based classifier for time series. Data Mining and Knowledge Discovery. 39(2).
3.
Tan, Chang Wei, Jiahao Meng, Baimei He, et al.. (2025). Effects of therapeutic interventions on long COVID: a meta-analysis of randomized controlled trials. EClinicalMedicine. 87. 103412–103412.
4.
Bagnall, Anthony, Matthew Middlehurst, Germain Forestier, et al.. (2024). A Hands-on Introduction to Time Series Classification and Regression. ePrints Soton (University of Southampton). 6410–6411. 2 indexed citations
5.
Foumani, Navid Mohammadi, Lynn Miller, Chang Wei Tan, et al.. (2024). Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey. ACM Computing Surveys. 56(9). 1–45. 80 indexed citations breakdown →
6.
Tan, Chang Wei, et al.. (2023). Ultra-fast meta-parameter optimization for time series similarity measures with application to nearest neighbour classification. Knowledge and Information Systems. 65(5). 2123–2157. 1 indexed citations
7.
Foumani, Navid Mohammadi, Chang Wei Tan, Geoffrey I. Webb, & Mahsa Salehi. (2023). Improving position encoding of transformers for multivariate time series classification. Data Mining and Knowledge Discovery. 38(1). 22–48. 73 indexed citations
8.
Tan, Chang Wei, et al.. (2023). Parameterizing the cost function of dynamic time warping with application to time series classification. Data Mining and Knowledge Discovery. 37(5). 2024–2045. 8 indexed citations
9.
Cao, Zijian, Dong Zhao, Haitao Yuan, et al.. (2023). F3VeTrac: Enabling Fine-Grained, Fully-Road-Covered, and Fully-Individual- Penetrative Vehicle Trajectory Recovery. IEEE Transactions on Mobile Computing. 23(5). 4975–4991. 4 indexed citations
10.
Gonen, Ofer M., Piero Perucca, Amanda Gilligan, et al.. (2022). Automated Interictal Epileptiform Discharge Detection from Scalp EEG Using Scalable Time-series Classification Approaches. International Journal of Neural Systems. 33(1). 2350001–2350001. 10 indexed citations
11.
Zhou, Qin, Zhihan Fang, Yunhuai Liu, Chang Wei Tan, & Desheng Zhang. (2021). A Measurement Framework for Explicit and Implicit Urban Traffic Sensing. ACM Transactions on Sensor Networks. 17(4). 1–27. 3 indexed citations
12.
Tan, Chang Wei, Christoph Bergmeir, François Petitjean, & Geoffrey I. Webb. (2021). Time series extrinsic regression: Predicting numeric values from time series data.. PubMed. 35(3). 1032–1060. 62 indexed citations
13.
Tan, Chang Wei, Christoph Bergmeir, François Petitjean, & Geoffrey I. Webb. (2020). Monash University, UEA, UCR Time Series Regression Archive. arXiv (Cornell University). 4 indexed citations
14.
Tan, Chang Wei, et al.. (2018). Tamping Effectiveness Prediction Using Supervised Machine Learning Techniques. 1010–1023. 2 indexed citations
15.
Zhou, Qin, Zhihan Fang, Yunhuai Liu, et al.. (2018). EXIMIUS. 1–14. 29 indexed citations
16.
Li, Qiang, et al.. (2016). A Study of Magnetic Resonance Wireless Power Transfer System Based on Half Bridge Inverter. 317. 1–5. 2 indexed citations
17.
Tan, Chang Wei, Qi Liu, Enhong Chen, Hui Xiong, & Xiang Wu. (2014). Object-Oriented Travel Package Recommendation. ACM Transactions on Intelligent Systems and Technology. 5(3). 1–26. 23 indexed citations
18.
Tan, Chang Wei & Jun Cao. (2013). An Algorithm for License Plate Location Based on Color and Texture. 7 a. 356–359. 3 indexed citations
19.
Tan, Chang Wei & Jun Cao. (2013). Periodicity and Permanence of a Discrete Impulsive Lotka-Volterra Predator-Prey Model Concerning Integrated Pest Management. Discrete Dynamics in Nature and Society. 2013. 1–10.
20.
Tan, Chang Wei & Robert DiBiano. (1972). A Parametric Study of Falkner-Skan Problem with Mass Transfer. AIAA Journal. 10(7). 923–925. 20 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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