Loading…
TimeSeer: Scagnostics for High-Dimensional Time Series
We introduce a method (Scagnostic time series) and an application (TimeSeer) for organizing multivariate time series and for guiding interactive exploration through high-dimensional data. The method is based on nine characterizations of the 2D distributions of orthogonal pairwise projections on a se...
Saved in:
| Published in: | IEEE transactions on visualization and computer graphics 2013-03, Vol.19 (3), p.470-483 |
|---|---|
| Main Authors: | , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c352t-5893ddfe1e19bbd1d7925849effc3dd01221ba35e9f85fc29494d8807d171b513 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c352t-5893ddfe1e19bbd1d7925849effc3dd01221ba35e9f85fc29494d8807d171b513 |
| container_end_page | 483 |
| container_issue | 3 |
| container_start_page | 470 |
| container_title | IEEE transactions on visualization and computer graphics |
| container_volume | 19 |
| creator | Tuan Nhon Dang Anand, A. Wilkinson, L. |
| description | We introduce a method (Scagnostic time series) and an application (TimeSeer) for organizing multivariate time series and for guiding interactive exploration through high-dimensional data. The method is based on nine characterizations of the 2D distributions of orthogonal pairwise projections on a set of points in multidimensional euclidean space. These characterizations include measures, such as, density, skewness, shape, outliers, and texture. Working directly with these Scagnostic measures, we can locate anomalous or interesting subseries for further analysis. Our application is designed to handle the types of doubly multivariate data series that are often found in security, financial, social, and other sectors. |
| doi_str_mv | 10.1109/TVCG.2012.128 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1109_TVCG_2012_128</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6200267</ieee_id><sourcerecordid>1273437187</sourcerecordid><originalsourceid>FETCH-LOGICAL-c352t-5893ddfe1e19bbd1d7925849effc3dd01221ba35e9f85fc29494d8807d171b513</originalsourceid><addsrcrecordid>eNo9kD1PwzAQhi0EoqUwMiGhjCwpPjvxBxsqH0WqxNDCGiXxuRjlo9jtwL_HUUunO-l97tXpIeQa6BSA6vvV5-x1yiiwKTB1QsagM0hpTsVp3KmUKRNMjMhFCN-UQpYpfU5GjHMqBcCYiJVrcYnoH5JlXa67PmxdHRLb-2Tu1l_pU4y74PqubJIBTZboHYZLcmbLJuDVYU7Ix8vzajZPF--vb7PHRVrznG3TXGlujEVA0FVlwEjNcpVptLaOQfyaQVXyHLVVua2ZznRmlKLSgIQqBz4hd_veje9_dhi2RetCjU1TdtjvQgFM8oxLUDKi6R6tfR-CR1tsvGtL_1sALQZVxaCqGFTFMxX520P1rmrRHOl_NxG42QMOEY-xYJQyIfkfl8ZrJw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1273437187</pqid></control><display><type>article</type><title>TimeSeer: Scagnostics for High-Dimensional Time Series</title><source>IEEE Electronic Library (IEL) Journals</source><creator>Tuan Nhon Dang ; Anand, A. ; Wilkinson, L.</creator><creatorcontrib>Tuan Nhon Dang ; Anand, A. ; Wilkinson, L.</creatorcontrib><description>We introduce a method (Scagnostic time series) and an application (TimeSeer) for organizing multivariate time series and for guiding interactive exploration through high-dimensional data. The method is based on nine characterizations of the 2D distributions of orthogonal pairwise projections on a set of points in multidimensional euclidean space. These characterizations include measures, such as, density, skewness, shape, outliers, and texture. Working directly with these Scagnostic measures, we can locate anomalous or interesting subseries for further analysis. Our application is designed to handle the types of doubly multivariate data series that are often found in security, financial, social, and other sectors.</description><identifier>ISSN: 1077-2626</identifier><identifier>EISSN: 1941-0506</identifier><identifier>DOI: 10.1109/TVCG.2012.128</identifier><identifier>PMID: 23307611</identifier><identifier>CODEN: ITVGEA</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Algorithms ; Computer Graphics ; Computer Simulation ; Density measurement ; Employment ; high-dimensional visual analytics ; Image Interpretation, Computer-Assisted - methods ; Imaging, Three-Dimensional - methods ; Length measurement ; Lenses ; Models, Statistical ; multiple time series ; Multivariate Analysis ; Reproducibility of Results ; Scagnostics ; scatterplot matrix ; Sensitivity and Specificity ; Shape ; Software ; Time series analysis ; User-Computer Interface ; Visualization</subject><ispartof>IEEE transactions on visualization and computer graphics, 2013-03, Vol.19 (3), p.470-483</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c352t-5893ddfe1e19bbd1d7925849effc3dd01221ba35e9f85fc29494d8807d171b513</citedby><cites>FETCH-LOGICAL-c352t-5893ddfe1e19bbd1d7925849effc3dd01221ba35e9f85fc29494d8807d171b513</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6200267$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,54771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23307611$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tuan Nhon Dang</creatorcontrib><creatorcontrib>Anand, A.</creatorcontrib><creatorcontrib>Wilkinson, L.</creatorcontrib><title>TimeSeer: Scagnostics for High-Dimensional Time Series</title><title>IEEE transactions on visualization and computer graphics</title><addtitle>TVCG</addtitle><addtitle>IEEE Trans Vis Comput Graph</addtitle><description>We introduce a method (Scagnostic time series) and an application (TimeSeer) for organizing multivariate time series and for guiding interactive exploration through high-dimensional data. The method is based on nine characterizations of the 2D distributions of orthogonal pairwise projections on a set of points in multidimensional euclidean space. These characterizations include measures, such as, density, skewness, shape, outliers, and texture. Working directly with these Scagnostic measures, we can locate anomalous or interesting subseries for further analysis. Our application is designed to handle the types of doubly multivariate data series that are often found in security, financial, social, and other sectors.</description><subject>Algorithms</subject><subject>Computer Graphics</subject><subject>Computer Simulation</subject><subject>Density measurement</subject><subject>Employment</subject><subject>high-dimensional visual analytics</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Length measurement</subject><subject>Lenses</subject><subject>Models, Statistical</subject><subject>multiple time series</subject><subject>Multivariate Analysis</subject><subject>Reproducibility of Results</subject><subject>Scagnostics</subject><subject>scatterplot matrix</subject><subject>Sensitivity and Specificity</subject><subject>Shape</subject><subject>Software</subject><subject>Time series analysis</subject><subject>User-Computer Interface</subject><subject>Visualization</subject><issn>1077-2626</issn><issn>1941-0506</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNo9kD1PwzAQhi0EoqUwMiGhjCwpPjvxBxsqH0WqxNDCGiXxuRjlo9jtwL_HUUunO-l97tXpIeQa6BSA6vvV5-x1yiiwKTB1QsagM0hpTsVp3KmUKRNMjMhFCN-UQpYpfU5GjHMqBcCYiJVrcYnoH5JlXa67PmxdHRLb-2Tu1l_pU4y74PqubJIBTZboHYZLcmbLJuDVYU7Ix8vzajZPF--vb7PHRVrznG3TXGlujEVA0FVlwEjNcpVptLaOQfyaQVXyHLVVua2ZznRmlKLSgIQqBz4hd_veje9_dhi2RetCjU1TdtjvQgFM8oxLUDKi6R6tfR-CR1tsvGtL_1sALQZVxaCqGFTFMxX520P1rmrRHOl_NxG42QMOEY-xYJQyIfkfl8ZrJw</recordid><startdate>20130301</startdate><enddate>20130301</enddate><creator>Tuan Nhon Dang</creator><creator>Anand, A.</creator><creator>Wilkinson, L.</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20130301</creationdate><title>TimeSeer: Scagnostics for High-Dimensional Time Series</title><author>Tuan Nhon Dang ; Anand, A. ; Wilkinson, L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c352t-5893ddfe1e19bbd1d7925849effc3dd01221ba35e9f85fc29494d8807d171b513</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Algorithms</topic><topic>Computer Graphics</topic><topic>Computer Simulation</topic><topic>Density measurement</topic><topic>Employment</topic><topic>high-dimensional visual analytics</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Length measurement</topic><topic>Lenses</topic><topic>Models, Statistical</topic><topic>multiple time series</topic><topic>Multivariate Analysis</topic><topic>Reproducibility of Results</topic><topic>Scagnostics</topic><topic>scatterplot matrix</topic><topic>Sensitivity and Specificity</topic><topic>Shape</topic><topic>Software</topic><topic>Time series analysis</topic><topic>User-Computer Interface</topic><topic>Visualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tuan Nhon Dang</creatorcontrib><creatorcontrib>Anand, A.</creatorcontrib><creatorcontrib>Wilkinson, L.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on visualization and computer graphics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tuan Nhon Dang</au><au>Anand, A.</au><au>Wilkinson, L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>TimeSeer: Scagnostics for High-Dimensional Time Series</atitle><jtitle>IEEE transactions on visualization and computer graphics</jtitle><stitle>TVCG</stitle><addtitle>IEEE Trans Vis Comput Graph</addtitle><date>2013-03-01</date><risdate>2013</risdate><volume>19</volume><issue>3</issue><spage>470</spage><epage>483</epage><pages>470-483</pages><issn>1077-2626</issn><eissn>1941-0506</eissn><coden>ITVGEA</coden><abstract>We introduce a method (Scagnostic time series) and an application (TimeSeer) for organizing multivariate time series and for guiding interactive exploration through high-dimensional data. The method is based on nine characterizations of the 2D distributions of orthogonal pairwise projections on a set of points in multidimensional euclidean space. These characterizations include measures, such as, density, skewness, shape, outliers, and texture. Working directly with these Scagnostic measures, we can locate anomalous or interesting subseries for further analysis. Our application is designed to handle the types of doubly multivariate data series that are often found in security, financial, social, and other sectors.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>23307611</pmid><doi>10.1109/TVCG.2012.128</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1077-2626 |
| ispartof | IEEE transactions on visualization and computer graphics, 2013-03, Vol.19 (3), p.470-483 |
| issn | 1077-2626 1941-0506 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_TVCG_2012_128 |
| source | IEEE Electronic Library (IEL) Journals |
| subjects | Algorithms Computer Graphics Computer Simulation Density measurement Employment high-dimensional visual analytics Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Length measurement Lenses Models, Statistical multiple time series Multivariate Analysis Reproducibility of Results Scagnostics scatterplot matrix Sensitivity and Specificity Shape Software Time series analysis User-Computer Interface Visualization |
| title | TimeSeer: Scagnostics for High-Dimensional Time Series |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-03T09%3A43%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=TimeSeer:%20Scagnostics%20for%20High-Dimensional%20Time%20Series&rft.jtitle=IEEE%20transactions%20on%20visualization%20and%20computer%20graphics&rft.au=Tuan%20Nhon%20Dang&rft.date=2013-03-01&rft.volume=19&rft.issue=3&rft.spage=470&rft.epage=483&rft.pages=470-483&rft.issn=1077-2626&rft.eissn=1941-0506&rft.coden=ITVGEA&rft_id=info:doi/10.1109/TVCG.2012.128&rft_dat=%3Cproquest_cross%3E1273437187%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c352t-5893ddfe1e19bbd1d7925849effc3dd01221ba35e9f85fc29494d8807d171b513%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1273437187&rft_id=info:pmid/23307611&rft_ieee_id=6200267&rfr_iscdi=true |