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Shaghayegh Gharghabi
Researcher at University of California, Riverside
Publications - 13
Citations - 857
Shaghayegh Gharghabi is an academic researcher from University of California, Riverside. The author has contributed to research in topics: Euclidean distance & Measure (mathematics). The author has an hindex of 7, co-authored 13 publications receiving 410 citations. Previous affiliations of Shaghayegh Gharghabi include Amirkabir University of Technology.
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The UCR time series archive
Hoang Anh Dau,Anthony J. Bagnall,Kaveh Kamgar,Chin-Chia Michael Yeh,Yan Zhu,Shaghayegh Gharghabi,Chotirat Ann Ratanamahatana,Eamonn Keogh +7 more
TL;DR: The UCR time series archive as discussed by the authors has become an important resource in the time series data mining community, with at least one thousand published papers making use of one data set from the archive.
Posted Content
The UCR Time Series Archive
Hoang Anh Dau,Anthony J. Bagnall,Kaveh Kamgar,Chin-Chia Michael Yeh,Yan Zhu,Shaghayegh Gharghabi,Chotirat Ann Ratanamahatana,Eamonn Keogh +7 more
TL;DR: A novel and yet actionable claim is made: of the hundreds of papers that show an improvement over the standard baseline ( 1-nearest neighbor classification ), a fraction might be mis-attributing the reasons for their improvement.
Proceedings ArticleDOI
Matrix Profile VIII: Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels
Shaghayegh Gharghabi,Yifei Ding,Chin-Chia Michael Yeh,Kaveh Kamgar,Liudmila Ulanova,Eamonn Keogh +5 more
TL;DR: This work presents an algorithm which is domain agnostic, has only one easily determined parameter, and can handle data streaming at a high rate, and is the first to show that semantic segmentation may be possible at superhuman performance levels.
Journal ArticleDOI
Domain agnostic online semantic segmentation for multi-dimensional time series
Shaghayegh Gharghabi,Chin-Chia Michael Yeh,Yifei Ding,Wei Ding,Paul R. Hibbing,Samuel R. LaMunion,Andrew S. Kaplan,Scott E. Crouter,Eamonn Keogh +8 more
TL;DR: A multi-dimensional algorithm is presented, which is domain agnostic, has only one, easily-determined parameter, and can handle data streaming at a high rate, and is tested on the largest and most diverse collection of time series datasets ever considered.
Proceedings ArticleDOI
Matrix Profile XII: MPdist: A Novel Time Series Distance Measure to Allow Data Mining in More Challenging Scenarios
TL;DR: This work introduces a novel distance measure MPdist and shows that this proposed distance measure is much more robust than current distance measures, and allows us to successfully mine datasets that would defeat any Euclidean or DTW distance-based algorithm.