Loading…
Rethinking Temporal Dependencies in Multiple Time Series: A Use Case in Financial Data
These days, complex systems yield copious time series data, necessitating understanding co-generation, often assessed through pairwise comparisons. However, this method lacks scalability and temporal dynamics handling. In this paper, we advocate using a temporal graph to capture contiguous effects a...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Conference Proceeding |
| Language: | English |
| Subjects: | |
| Online Access: | Request full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | |
|---|---|
| cites | |
| container_end_page | 1252 |
| container_issue | |
| container_start_page | 1247 |
| container_title | |
| container_volume | |
| creator | Owusu, Patrick Asante Tajeuna, Etienne Patenaude, Jean-Marc Brun, Armelle Wang, Shengrui |
| description | These days, complex systems yield copious time series data, necessitating understanding co-generation, often assessed through pairwise comparisons. However, this method lacks scalability and temporal dynamics handling. In this paper, we advocate using a temporal graph to capture contiguous effects among multiple time series efficiently. Our two-step approach identifies patterns and temporal influences with low execution time, showcasing its potential in financial system incident prediction. |
| doi_str_mv | 10.1109/ICDM58522.2023.00156 |
| format | conference_proceeding |
| fullrecord | <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_10415712</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10415712</ieee_id><sourcerecordid>10415712</sourcerecordid><originalsourceid>FETCH-LOGICAL-i250t-6a698efd130a2c771bf5dd6d0cc17b54cc00ed0b5107d7d44b09a196fc296d983</originalsourceid><addsrcrecordid>eNotj89Kw0AYxFdBsNa-QQ_7Aqnf_t_1VlKrhRZBU69ls_tFV5MYknjw7Y3oZQaG-Q0MIUsGK8bA3ezyzUFZxfmKAxcrAKb0GVk446xQIMBYa87JjAsjMyutviRXw_AOILQWMCMvTzi-pfYjta-0wKb77H1NN9hhG7ENCQeaWnr4qsfU1UiL1CB9xn7Kb-maHgekuZ9k6mxT6yfgl_ajvyYXla8HXPz7nBy3d0X-kO0f73f5ep8lrmDMtNfOYhWZAM-DMaysVIw6QgjMlEqGAIARSsXARBOlLMF55nQVuNNxejgny7_dhIinrk-N779PDCRThnHxAwAOUVQ</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Rethinking Temporal Dependencies in Multiple Time Series: A Use Case in Financial Data</title><source>IEEE Xplore All Conference Series</source><creator>Owusu, Patrick Asante ; Tajeuna, Etienne ; Patenaude, Jean-Marc ; Brun, Armelle ; Wang, Shengrui</creator><creatorcontrib>Owusu, Patrick Asante ; Tajeuna, Etienne ; Patenaude, Jean-Marc ; Brun, Armelle ; Wang, Shengrui</creatorcontrib><description>These days, complex systems yield copious time series data, necessitating understanding co-generation, often assessed through pairwise comparisons. However, this method lacks scalability and temporal dynamics handling. In this paper, we advocate using a temporal graph to capture contiguous effects among multiple time series efficiently. Our two-step approach identifies patterns and temporal influences with low execution time, showcasing its potential in financial system incident prediction.</description><identifier>EISSN: 2374-8486</identifier><identifier>EISBN: 9798350307887</identifier><identifier>DOI: 10.1109/ICDM58522.2023.00156</identifier><identifier>CODEN: IEEPAD</identifier><language>eng</language><publisher>IEEE</publisher><subject>Complex systems ; Correlation ; Cross-sectional patterns ; Data mining ; Data models ; Financial data ; Multiple time series ; Scalability ; Series trajectory ; Temporal graph ; Time series analysis</subject><ispartof>2023 IEEE International Conference on Data Mining (ICDM), 2023, p.1247-1252</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10415712$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,27924,54554,54931</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10415712$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Owusu, Patrick Asante</creatorcontrib><creatorcontrib>Tajeuna, Etienne</creatorcontrib><creatorcontrib>Patenaude, Jean-Marc</creatorcontrib><creatorcontrib>Brun, Armelle</creatorcontrib><creatorcontrib>Wang, Shengrui</creatorcontrib><title>Rethinking Temporal Dependencies in Multiple Time Series: A Use Case in Financial Data</title><title>2023 IEEE International Conference on Data Mining (ICDM)</title><addtitle>ICDM</addtitle><description>These days, complex systems yield copious time series data, necessitating understanding co-generation, often assessed through pairwise comparisons. However, this method lacks scalability and temporal dynamics handling. In this paper, we advocate using a temporal graph to capture contiguous effects among multiple time series efficiently. Our two-step approach identifies patterns and temporal influences with low execution time, showcasing its potential in financial system incident prediction.</description><subject>Complex systems</subject><subject>Correlation</subject><subject>Cross-sectional patterns</subject><subject>Data mining</subject><subject>Data models</subject><subject>Financial data</subject><subject>Multiple time series</subject><subject>Scalability</subject><subject>Series trajectory</subject><subject>Temporal graph</subject><subject>Time series analysis</subject><issn>2374-8486</issn><isbn>9798350307887</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2023</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj89Kw0AYxFdBsNa-QQ_7Aqnf_t_1VlKrhRZBU69ls_tFV5MYknjw7Y3oZQaG-Q0MIUsGK8bA3ezyzUFZxfmKAxcrAKb0GVk446xQIMBYa87JjAsjMyutviRXw_AOILQWMCMvTzi-pfYjta-0wKb77H1NN9hhG7ENCQeaWnr4qsfU1UiL1CB9xn7Kb-maHgekuZ9k6mxT6yfgl_ajvyYXla8HXPz7nBy3d0X-kO0f73f5ep8lrmDMtNfOYhWZAM-DMaysVIw6QgjMlEqGAIARSsXARBOlLMF55nQVuNNxejgny7_dhIinrk-N779PDCRThnHxAwAOUVQ</recordid><startdate>20230101</startdate><enddate>20230101</enddate><creator>Owusu, Patrick Asante</creator><creator>Tajeuna, Etienne</creator><creator>Patenaude, Jean-Marc</creator><creator>Brun, Armelle</creator><creator>Wang, Shengrui</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20230101</creationdate><title>Rethinking Temporal Dependencies in Multiple Time Series: A Use Case in Financial Data</title><author>Owusu, Patrick Asante ; Tajeuna, Etienne ; Patenaude, Jean-Marc ; Brun, Armelle ; Wang, Shengrui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i250t-6a698efd130a2c771bf5dd6d0cc17b54cc00ed0b5107d7d44b09a196fc296d983</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Complex systems</topic><topic>Correlation</topic><topic>Cross-sectional patterns</topic><topic>Data mining</topic><topic>Data models</topic><topic>Financial data</topic><topic>Multiple time series</topic><topic>Scalability</topic><topic>Series trajectory</topic><topic>Temporal graph</topic><topic>Time series analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Owusu, Patrick Asante</creatorcontrib><creatorcontrib>Tajeuna, Etienne</creatorcontrib><creatorcontrib>Patenaude, Jean-Marc</creatorcontrib><creatorcontrib>Brun, Armelle</creatorcontrib><creatorcontrib>Wang, Shengrui</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Owusu, Patrick Asante</au><au>Tajeuna, Etienne</au><au>Patenaude, Jean-Marc</au><au>Brun, Armelle</au><au>Wang, Shengrui</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Rethinking Temporal Dependencies in Multiple Time Series: A Use Case in Financial Data</atitle><btitle>2023 IEEE International Conference on Data Mining (ICDM)</btitle><stitle>ICDM</stitle><date>2023-01-01</date><risdate>2023</risdate><spage>1247</spage><epage>1252</epage><pages>1247-1252</pages><eissn>2374-8486</eissn><eisbn>9798350307887</eisbn><coden>IEEPAD</coden><abstract>These days, complex systems yield copious time series data, necessitating understanding co-generation, often assessed through pairwise comparisons. However, this method lacks scalability and temporal dynamics handling. In this paper, we advocate using a temporal graph to capture contiguous effects among multiple time series efficiently. Our two-step approach identifies patterns and temporal influences with low execution time, showcasing its potential in financial system incident prediction.</abstract><pub>IEEE</pub><doi>10.1109/ICDM58522.2023.00156</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | EISSN: 2374-8486 |
| ispartof | 2023 IEEE International Conference on Data Mining (ICDM), 2023, p.1247-1252 |
| issn | 2374-8486 |
| language | eng |
| recordid | cdi_ieee_primary_10415712 |
| source | IEEE Xplore All Conference Series |
| subjects | Complex systems Correlation Cross-sectional patterns Data mining Data models Financial data Multiple time series Scalability Series trajectory Temporal graph Time series analysis |
| title | Rethinking Temporal Dependencies in Multiple Time Series: A Use Case in Financial Data |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T17%3A34%3A01IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Rethinking%20Temporal%20Dependencies%20in%20Multiple%20Time%20Series:%20A%20Use%20Case%20in%20Financial%20Data&rft.btitle=2023%20IEEE%20International%20Conference%20on%20Data%20Mining%20(ICDM)&rft.au=Owusu,%20Patrick%20Asante&rft.date=2023-01-01&rft.spage=1247&rft.epage=1252&rft.pages=1247-1252&rft.eissn=2374-8486&rft.coden=IEEPAD&rft_id=info:doi/10.1109/ICDM58522.2023.00156&rft.eisbn=9798350307887&rft_dat=%3Cieee_CHZPO%3E10415712%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i250t-6a698efd130a2c771bf5dd6d0cc17b54cc00ed0b5107d7d44b09a196fc296d983%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=10415712&rfr_iscdi=true |