Jason Lines

4.5k total citations · 4 hit papers
15 papers, 2.5k citations indexed

About

Jason Lines is a scholar working on Signal Processing, Economics and Econometrics and Artificial Intelligence. According to data from OpenAlex, Jason Lines has authored 15 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Signal Processing, 6 papers in Economics and Econometrics and 5 papers in Artificial Intelligence. Recurrent topics in Jason Lines's work include Time Series Analysis and Forecasting (10 papers), Music and Audio Processing (6 papers) and Complex Systems and Time Series Analysis (6 papers). Jason Lines is often cited by papers focused on Time Series Analysis and Forecasting (10 papers), Music and Audio Processing (6 papers) and Complex Systems and Time Series Analysis (6 papers). Jason Lines collaborates with scholars based in United Kingdom and United States. Jason Lines's co-authors include Anthony Bagnall, Aaron Bostrom, Jon Hills, James Large, Eamonn Keogh, Sarah Taylor, Luke M. Davis, James Mapp, Barry-John Theobald and Andoni P. Toms and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering, Data Mining and Knowledge Discovery and International Journal of Neural Systems.

In The Last Decade

Jason Lines

15 papers receiving 2.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances

2016 801 citations Anthony Bagnall, Jason Lines et al. Data Mining and Knowledge Discovery profile →
Time series classification with ensembles of elastic distance measures

2014 333 citations Jason Lines, Anthony Bagnall Data Mining and Knowledge Discovery profile →
Classification of time series by shapelet transformation

2013 330 citations Jon Hills, Jason Lines et al. Data Mining and Knowledge Discovery profile →
Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles

2015 277 citations Anthony Bagnall, Jason Lines et al. IEEE Transactions on Knowledge and Data Engineering profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jason Lines United Kingdom	12	2.0k	1.6k	487	201	190	15	2.5k
Anthony Bagnall United Kingdom	26	2.6k 1.3×	2.3k 1.4×	688 1.4×	293 1.5×	253 1.3×	56	3.8k
Thanawin Rakthanmanon United States	16	1.2k 0.6×	879 0.5×	262 0.5×	230 1.1×	80 0.4×	35	1.7k
James Large United Kingdom	8	909 0.5×	750 0.5×	202 0.4×	105 0.5×	119 0.6×	8	1.3k
Aaron Bostrom United Kingdom	6	885 0.4×	735 0.4×	193 0.4×	103 0.5×	102 0.5×	7	1.4k
Jesin Zakaria United States	10	917 0.5×	611 0.4×	187 0.4×	175 0.9×	62 0.3×	11	1.2k
John Paparrizos United States	18	893 0.4×	891 0.5×	233 0.5×	108 0.5×	28 0.1×	44	1.5k
Eugene Tuv United States	10	506 0.3×	637 0.4×	127 0.3×	161 0.8×	85 0.4×	23	1.2k
Weizhong Yan United States	16	696 0.3×	866 0.5×	85 0.2×	197 1.0×	210 1.1×	62	2.0k
Kenneth W. Bauer United States	19	175 0.1×	553 0.3×	79 0.2×	237 1.2×	92 0.5×	119	1.3k

Countries citing papers authored by Jason Lines

Since Specialization

Citations

This map shows the geographic impact of Jason Lines'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 Jason Lines with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jason Lines more than expected).

Fields of papers citing papers by Jason Lines

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jason Lines. 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 Jason Lines. The network helps show where Jason Lines may publish in the future.

Co-authorship network of co-authors of Jason Lines

This figure shows the co-authorship network connecting the top 25 collaborators of Jason Lines. A scholar is included among the top collaborators of Jason Lines 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 Jason Lines. Jason Lines 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:

15 of 15 papers shown

Johnson, J. H., et al.. (2024). Identifying earthquake swarms at Mt. Ruapehu, New Zealand: a machine learning approach. Frontiers in Earth Science. 12. 2 indexed citations

Large, James, Jason Lines, & Anthony Bagnall. (2019). A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates. Data Mining and Knowledge Discovery. 33(6). 1674–1709. 54 indexed citations

Lines, Jason, et al.. (2019). Detecting right whales from autonomous surface vehicles using RNNs and CNNs. UEA Digital Repository (University of East Anglia). 3 indexed citations

Milner, Ben, et al.. (2019). A comparison of machine learning methods for detecting right whales from autonomous surface vehicles. UEA Digital Repository (University of East Anglia). 1–5. 4 indexed citations

Lines, Jason, Sarah Taylor, & Anthony Bagnall. (2018). Time Series Classification with HIVE-COTE. ACM Transactions on Knowledge Discovery from Data. 12(5). 1–35. 206 indexed citations

Bagnall, Anthony, Jason Lines, Aaron Bostrom, James Large, & Eamonn Keogh. (2016). The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining and Knowledge Discovery. 31(3). 606–660. 801 indexed citations breakdown →

Bagnall, Anthony, Jason Lines, Jon Hills, & Aaron Bostrom. (2016). Time-series classification with COTE: The collective of transformation-based ensembles. UEA Digital Repository (University of East Anglia). 46 indexed citations

Lines, Jason, Sarah Taylor, & Anthony Bagnall. (2016). HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles for Time Series Classification. UEA Digital Repository (University of East Anglia). 1041–1046. 103 indexed citations

Bagnall, Anthony, Jason Lines, Jon Hills, & Aaron Bostrom. (2015). Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles. IEEE Transactions on Knowledge and Data Engineering. 27(9). 2522–2535. 277 indexed citations breakdown →

10.

Lines, Jason & Anthony Bagnall. (2014). Ensembles of Elastic Distance Measures for Time Series Classification. 17 indexed citations

11.

Lines, Jason & Anthony Bagnall. (2014). Time series classification with ensembles of elastic distance measures. Data Mining and Knowledge Discovery. 29(3). 565–592. 333 indexed citations breakdown →

12.

Hills, Jon, et al.. (2013). Classification of time series by shapelet transformation. Data Mining and Knowledge Discovery. 28(4). 851–881. 330 indexed citations breakdown →

13.

Bagnall, Anthony, Luke M. Davis, Jon Hills, & Jason Lines. (2012). Transformation Based Ensembles for Time Series Classification. 307–318. 65 indexed citations

14.

Lines, Jason, Luke M. Davis, Jon Hills, & Anthony Bagnall. (2012). A shapelet transform for time series classification. 289–297. 237 indexed citations

15.

Davis, Luke M., Barry-John Theobald, Jason Lines, Andoni P. Toms, & Anthony Bagnall. (2012). ON THE SEGMENTATION AND CLASSIFICATION OF HAND RADIOGRAPHS. International Journal of Neural Systems. 22(5). 1250020–1250020. 13 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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