Time series prediction evolving Voronoi regions
Time series prediction is a complex problem that consists of forecasting the future behavior of a set of data with the only information of the previous data. The main problem is the fact that most of the time series that represent real phenomena include local behaviors that cannot be modelled by glo...
Gespeichert in:
| Veröffentlicht in: | Applied intelligence (Dordrecht, Netherlands) Netherlands), 2011-02, Vol.34 (1), p.116-126 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 126 |
|---|---|
| container_issue | 1 |
| container_start_page | 116 |
| container_title | Applied intelligence (Dordrecht, Netherlands) |
| container_volume | 34 |
| creator | Luque, Cristobal Valls, Jose M. Isasi, Pedro |
| description | Time series prediction is a complex problem that consists of forecasting the future behavior of a set of data with the only information of the previous data. The main problem is the fact that most of the time series that represent real phenomena include local behaviors that cannot be modelled by global approaches. This work presents a new procedure able to find predictable local behaviors, and thus, attaining a better level of total prediction. This new method is based on a division of the input space into Voronoi regions by means of Evolution Strategies. Our method has been tested using different time series domains. One of them that represents the water demand in a water tank, through a long period of time. The other two domains are well known examples of chaotic time series (Mackey-Glass) and natural phenomenon time series (Sunspot). Results prove that, in most of cases, the proposed algorithm obtain better results than other algorithms commonly used. |
| doi_str_mv | 10.1007/s10489-009-0184-9 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_907946526</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2247462581</sourcerecordid><originalsourceid>FETCH-LOGICAL-c390t-16a691f2a33a9bf21bc60a4b3d35c3f7f683add9a4c1e32298472973090ccc313</originalsourceid><addsrcrecordid>eNp1kE1LAzEQhoMoWKs_wNvixdPayccmO0cpfkHBSxVvIc1mS8p2U5NtwX9vygqC4GEYGJ73ZXgIuaZwRwHULFEQNZYAeWgtSjwhE1opXiqB6pRMAJkopcSPc3KR0gYAOAc6IbOl37oiuehdKnbRNd4OPvSFO4Tu4Pt18R5i6IMvolvne7okZ63pkrv62VPy9viwnD-Xi9enl_n9orQcYSipNBJpywznBlctoysrwYgVb3hleataWXPTNGiEpY4zhrVQDBUHBGstp3xKbsfeXQyfe5cGvfXJuq4zvQv7pBEUClkxmcmbP-Qm7GOfn9O5lELFWZUhOkI2hpSia_Uu-q2JX5qCPgrUo0CdBeqjQI05w8ZMymy_dvG3-P_QN7Z7cc8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>847105325</pqid></control><display><type>article</type><title>Time series prediction evolving Voronoi regions</title><source>Springer Nature - Complete Springer Journals</source><creator>Luque, Cristobal ; Valls, Jose M. ; Isasi, Pedro</creator><creatorcontrib>Luque, Cristobal ; Valls, Jose M. ; Isasi, Pedro</creatorcontrib><description>Time series prediction is a complex problem that consists of forecasting the future behavior of a set of data with the only information of the previous data. The main problem is the fact that most of the time series that represent real phenomena include local behaviors that cannot be modelled by global approaches. This work presents a new procedure able to find predictable local behaviors, and thus, attaining a better level of total prediction. This new method is based on a division of the input space into Voronoi regions by means of Evolution Strategies. Our method has been tested using different time series domains. One of them that represents the water demand in a water tank, through a long period of time. The other two domains are well known examples of chaotic time series (Mackey-Glass) and natural phenomenon time series (Sunspot). Results prove that, in most of cases, the proposed algorithm obtain better results than other algorithms commonly used.</description><identifier>ISSN: 0924-669X</identifier><identifier>EISSN: 1573-7497</identifier><identifier>DOI: 10.1007/s10489-009-0184-9</identifier><language>eng</language><publisher>Boston: Springer US</publisher><subject>Algorithms ; Artificial Intelligence ; Behavior ; Computer Science ; Division ; Evolution ; Gene expression ; Genetic algorithms ; Intelligence ; Machine learning ; Machines ; Manufacturing ; Mathematical models ; Mechanical Engineering ; Methods ; Neural networks ; Processes ; Prototypes ; Regression analysis ; Strategy ; System theory ; Time series ; Water tanks</subject><ispartof>Applied intelligence (Dordrecht, Netherlands), 2011-02, Vol.34 (1), p.116-126</ispartof><rights>Springer Science+Business Media, LLC 2009</rights><rights>Springer Science+Business Media, LLC 2011</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c390t-16a691f2a33a9bf21bc60a4b3d35c3f7f683add9a4c1e32298472973090ccc313</citedby><cites>FETCH-LOGICAL-c390t-16a691f2a33a9bf21bc60a4b3d35c3f7f683add9a4c1e32298472973090ccc313</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10489-009-0184-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10489-009-0184-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Luque, Cristobal</creatorcontrib><creatorcontrib>Valls, Jose M.</creatorcontrib><creatorcontrib>Isasi, Pedro</creatorcontrib><title>Time series prediction evolving Voronoi regions</title><title>Applied intelligence (Dordrecht, Netherlands)</title><addtitle>Appl Intell</addtitle><description>Time series prediction is a complex problem that consists of forecasting the future behavior of a set of data with the only information of the previous data. The main problem is the fact that most of the time series that represent real phenomena include local behaviors that cannot be modelled by global approaches. This work presents a new procedure able to find predictable local behaviors, and thus, attaining a better level of total prediction. This new method is based on a division of the input space into Voronoi regions by means of Evolution Strategies. Our method has been tested using different time series domains. One of them that represents the water demand in a water tank, through a long period of time. The other two domains are well known examples of chaotic time series (Mackey-Glass) and natural phenomenon time series (Sunspot). Results prove that, in most of cases, the proposed algorithm obtain better results than other algorithms commonly used.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Behavior</subject><subject>Computer Science</subject><subject>Division</subject><subject>Evolution</subject><subject>Gene expression</subject><subject>Genetic algorithms</subject><subject>Intelligence</subject><subject>Machine learning</subject><subject>Machines</subject><subject>Manufacturing</subject><subject>Mathematical models</subject><subject>Mechanical Engineering</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Processes</subject><subject>Prototypes</subject><subject>Regression analysis</subject><subject>Strategy</subject><subject>System theory</subject><subject>Time series</subject><subject>Water tanks</subject><issn>0924-669X</issn><issn>1573-7497</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kE1LAzEQhoMoWKs_wNvixdPayccmO0cpfkHBSxVvIc1mS8p2U5NtwX9vygqC4GEYGJ73ZXgIuaZwRwHULFEQNZYAeWgtSjwhE1opXiqB6pRMAJkopcSPc3KR0gYAOAc6IbOl37oiuehdKnbRNd4OPvSFO4Tu4Pt18R5i6IMvolvne7okZ63pkrv62VPy9viwnD-Xi9enl_n9orQcYSipNBJpywznBlctoysrwYgVb3hleataWXPTNGiEpY4zhrVQDBUHBGstp3xKbsfeXQyfe5cGvfXJuq4zvQv7pBEUClkxmcmbP-Qm7GOfn9O5lELFWZUhOkI2hpSia_Uu-q2JX5qCPgrUo0CdBeqjQI05w8ZMymy_dvG3-P_QN7Z7cc8</recordid><startdate>20110201</startdate><enddate>20110201</enddate><creator>Luque, Cristobal</creator><creator>Valls, Jose M.</creator><creator>Isasi, Pedro</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PSYQQ</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20110201</creationdate><title>Time series prediction evolving Voronoi regions</title><author>Luque, Cristobal ; Valls, Jose M. ; Isasi, Pedro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c390t-16a691f2a33a9bf21bc60a4b3d35c3f7f683add9a4c1e32298472973090ccc313</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Behavior</topic><topic>Computer Science</topic><topic>Division</topic><topic>Evolution</topic><topic>Gene expression</topic><topic>Genetic algorithms</topic><topic>Intelligence</topic><topic>Machine learning</topic><topic>Machines</topic><topic>Manufacturing</topic><topic>Mathematical models</topic><topic>Mechanical Engineering</topic><topic>Methods</topic><topic>Neural networks</topic><topic>Processes</topic><topic>Prototypes</topic><topic>Regression analysis</topic><topic>Strategy</topic><topic>System theory</topic><topic>Time series</topic><topic>Water tanks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Luque, Cristobal</creatorcontrib><creatorcontrib>Valls, Jose M.</creatorcontrib><creatorcontrib>Isasi, Pedro</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest One Psychology</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Applied intelligence (Dordrecht, Netherlands)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Luque, Cristobal</au><au>Valls, Jose M.</au><au>Isasi, Pedro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Time series prediction evolving Voronoi regions</atitle><jtitle>Applied intelligence (Dordrecht, Netherlands)</jtitle><stitle>Appl Intell</stitle><date>2011-02-01</date><risdate>2011</risdate><volume>34</volume><issue>1</issue><spage>116</spage><epage>126</epage><pages>116-126</pages><issn>0924-669X</issn><eissn>1573-7497</eissn><abstract>Time series prediction is a complex problem that consists of forecasting the future behavior of a set of data with the only information of the previous data. The main problem is the fact that most of the time series that represent real phenomena include local behaviors that cannot be modelled by global approaches. This work presents a new procedure able to find predictable local behaviors, and thus, attaining a better level of total prediction. This new method is based on a division of the input space into Voronoi regions by means of Evolution Strategies. Our method has been tested using different time series domains. One of them that represents the water demand in a water tank, through a long period of time. The other two domains are well known examples of chaotic time series (Mackey-Glass) and natural phenomenon time series (Sunspot). Results prove that, in most of cases, the proposed algorithm obtain better results than other algorithms commonly used.</abstract><cop>Boston</cop><pub>Springer US</pub><doi>10.1007/s10489-009-0184-9</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0924-669X |
| ispartof | Applied intelligence (Dordrecht, Netherlands), 2011-02, Vol.34 (1), p.116-126 |
| issn | 0924-669X 1573-7497 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_907946526 |
| source | Springer Nature - Complete Springer Journals |
| subjects | Algorithms Artificial Intelligence Behavior Computer Science Division Evolution Gene expression Genetic algorithms Intelligence Machine learning Machines Manufacturing Mathematical models Mechanical Engineering Methods Neural networks Processes Prototypes Regression analysis Strategy System theory Time series Water tanks |
| title | Time series prediction evolving Voronoi regions |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-12T21%3A59%3A36IST&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=Time%20series%20prediction%20evolving%20Voronoi%20regions&rft.jtitle=Applied%20intelligence%20(Dordrecht,%20Netherlands)&rft.au=Luque,%20Cristobal&rft.date=2011-02-01&rft.volume=34&rft.issue=1&rft.spage=116&rft.epage=126&rft.pages=116-126&rft.issn=0924-669X&rft.eissn=1573-7497&rft_id=info:doi/10.1007/s10489-009-0184-9&rft_dat=%3Cproquest_cross%3E2247462581%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=847105325&rft_id=info:pmid/&rfr_iscdi=true |