Earo Wang
Impact in
-
- Forecasting Techniques and Applications
- Stock Market Forecasting Methods
- Signal Processing top 10%
- Time Series Analysis and Forecasting
Papers in
-
- Data Analysis with R 3
- Advanced Computational Techniques and Applications 1
-
- Time Series Analysis and Forecasting 4
- Co-authors
- Rob J. Hyndman (8 shared papers)Nikolay Laptev (1 shared paper)Alan J. Lee (1 shared paper)Dianne Cook (3 shared papers)Mitchell O’Hara-Wild (3 shared papers)Gabriel A. Caceres (1 shared paper)George Athanasopoulos (1 shared paper)Christoph Bergmeir (1 shared paper)
- Journals
- Journal of Computational and Graphical Statistics (2 papers)Computational Statistics & Data Analysis (1 paper)Wiley Interdisciplinary Reviews Computational Statistics (1 paper)The R Journal (1 paper)
- Partner nations
- AustraliaNew ZealandUnited Kingdom
In The Last Decade
Earo Wang
9 papers receiving 308 citations
Peers
Comparison fields: 5 of 91
- Management Science and Operations Research 109
- Signal Processing 85
- Artificial Intelligence 105
- Statistics and Probability 16
- Economics and Econometrics 50
Countries citing papers authored by Earo Wang
This map shows the geographic impact of Earo 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 Earo Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Earo Wang more than expected).
Fields of papers citing papers by Earo Wang
This network shows the impact of papers produced by Earo 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 Earo Wang. The network helps show where Earo Wang may publish in the future.
Co-authors
The 17 scholars most cited alongside Earo 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 | 2015 | 139 | |
| 2 | 2015 | 100 | |
| 3 | 2019 | 33 | |
| 4 | Forecasting Functions for Time Series and Linear Models [R package forecast version 8.13] | 2020 | 33 |
| 5 | Time Series Feature Extraction [R package tsfeatures version 1.0.2] | 2020 | 5 |
| 6 | 2020 | 5 | |
| 7 | 2021 | 2 | |
| 8 | Hierarchical and Grouped Time Series [R package hts version 6.0.1] | 2020 | 1 |
| 9 | Core Tools for Packages in the 'fable' Framework [R package fabletools version 0.3.0] | 2021 | 1 |
| 10 | 2023 | 0 |
About Earo Wang
Earo Wang is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Management Science and Operations Research and Ecology, having authored 10 papers that have together received 319 indexed citations. Recurring topics across this work include Time Series Analysis and Forecasting (4 papers), Data Analysis with R (3 papers), Data Visualization and Analytics (2 papers), Complex Systems and Time Series Analysis (1 paper), Advanced Computational Techniques and Applications (1 paper), Big Data Technologies and Applications (1 paper), Forecasting Techniques and Applications (1 paper) and Stock Market Forecasting Methods (1 paper). The work is most often cited by research in Management Science and Operations Research (109 citations), Signal Processing (85 citations), Artificial Intelligence (105 citations), Statistics and Probability (16 citations) and Economics and Econometrics (50 citations). Earo Wang has collaborated with scholars based in Australia, New Zealand and United Kingdom. Frequent co-authors include Rob J. Hyndman, Nikolay Laptev, Alan J. Lee, Dianne Cook, Mitchell O’Hara-Wild, Gabriel A. Caceres, George Athanasopoulos, Christoph Bergmeir, Fotios Petropoulos and Farah Yasmeen. Their work appears in journals such as Journal of Computational and Graphical Statistics, Computational Statistics & Data Analysis, Wiley Interdisciplinary Reviews Computational Statistics and The R Journal.
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.