Earo Wang

1.4k total citations
10 papers, 319 citations indexed

About

Earo Wang is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Earo Wang has authored 10 papers receiving a total of 319 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Signal Processing and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Earo Wang's work include Time Series Analysis and Forecasting (4 papers), Data Analysis with R (3 papers) and Data Visualization and Analytics (2 papers). Earo Wang is often cited by papers focused on Time Series Analysis and Forecasting (4 papers), Data Analysis with R (3 papers) and Data Visualization and Analytics (2 papers). Earo Wang collaborates with scholars based in Australia, New Zealand and Austria. Earo Wang's co-authors include Rob J. Hyndman, Nikolay Laptev, Alan J. Lee, Dianne Cook, Mitchell O’Hara-Wild, Gabriel A. Caceres, George Athanasopoulos, Farah Yasmeen, Fotios Petropoulos and Christoph Bergmeir and has published in prestigious journals such as Computational Statistics & Data Analysis, Journal of Computational and Graphical Statistics and The R Journal.

In The Last Decade

Earo Wang

9 papers receiving 308 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Earo Wang Australia	6	109	105	85	71	50	10	319
Silvia Bianconcini Italy	9	56 0.5×	47 0.4×	42 0.5×	30 0.4×	71 1.4×	26	320
K. Senthamarai Kannan India	10	74 0.7×	92 0.9×	28 0.3×	21 0.3×	47 0.9×	45	372
Estela Bee Dagum Italy	13	164 1.5×	69 0.7×	78 0.9×	56 0.8×	190 3.8×	43	580
Mansi Ghodsi United Kingdom	10	64 0.6×	53 0.5×	28 0.3×	16 0.2×	62 1.2×	25	338
Hasnain Iftikhar Pakistan	18	254 2.3×	108 1.0×	29 0.3×	285 4.0×	108 2.2×	41	661
Thiyanga S. Talagala Australia	4	130 1.2×	44 0.4×	68 0.8×	46 0.6×	29 0.6×	6	210
Rui Ren China	7	133 1.2×	96 0.9×	12 0.1×	49 0.7×	71 1.4×	27	297
Tomáš Cipra Czechia	9	79 0.7×	57 0.5×	30 0.4×	25 0.4×	42 0.8×	51	281
Bikash Sadhukhan India	10	135 1.2×	107 1.0×	22 0.3×	63 0.9×	43 0.9×	34	317
Nawa Raj Pokhrel United States	8	161 1.5×	32 0.3×	27 0.3×	80 1.1×	77 1.5×	18	277

Countries citing papers authored by Earo Wang

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Earo Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Earo Wang. A scholar is included among the top collaborators of Earo Wang 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 Earo Wang. Earo Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Wang, Earo, et al.. (2023). Taking the Scenic Route: Interactive and Performant Tour Animations. The R Journal. 15(2). 307–329.

2.

Lee, Stuart, et al.. (2021). The state‐of‐the‐art on tours for dynamic visualization of high‐dimensional data. Wiley Interdisciplinary Reviews Computational Statistics. 14(4). 2 indexed citations

3.

O’Hara-Wild, Mitchell, Rob J. Hyndman, & Earo Wang. (2021). Core Tools for Packages in the 'fable' Framework [R package fabletools version 0.3.0]. 1 indexed citations

4.

Hyndman, Rob J., George Athanasopoulos, Christoph Bergmeir, et al.. (2020). Forecasting Functions for Time Series and Linear Models [R package forecast version 8.13]. 33 indexed citations

5.

Hyndman, Rob J., Yanfei Kang, Pablo Montero‐Manso, et al.. (2020). Time Series Feature Extraction [R package tsfeatures version 1.0.2]. 5 indexed citations

6.

Wang, Earo, Dianne Cook, & Rob J. Hyndman. (2020). Calendar-Based Graphics for Visualizing People’s Daily Schedules. Journal of Computational and Graphical Statistics. 29(3). 490–502. 5 indexed citations

7.

Hyndman, Rob J., Alan Lee, Earo Wang, & Shanika L. Wickramasuriya. (2020). Hierarchical and Grouped Time Series [R package hts version 6.0.1]. 1 indexed citations

8.

Wang, Earo, Dianne Cook, & Rob J. Hyndman. (2019). A New Tidy Data Structure to Support Exploration and Modeling of Temporal Data. Journal of Computational and Graphical Statistics. 29(3). 466–478. 33 indexed citations

9.

Hyndman, Rob J., Earo Wang, & Nikolay Laptev. (2015). Large-Scale Unusual Time Series Detection. 1616–1619. 139 indexed citations

10.

Hyndman, Rob J., Alan J. Lee, & Earo Wang. (2015). Fast computation of reconciled forecasts for hierarchical and grouped time series. Computational Statistics & Data Analysis. 97. 16–32. 100 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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