Loading…
Functional Samples and Bootstrap for Predicting Sulfur Dioxide Levels
In this article we give enhancements of several functional techniques to forecast sulfur dioxide levels near a power plant. The data are considered as a time series of curves. Assuming a lag-one dependence, the predictions are computed using the functional kernel (with local bandwith) and the linear...
Saved in:
| Published in: | Technometrics 2005-05, Vol.47 (2), p.212-222 |
|---|---|
| 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-c380t-14088b35fb1b81d402b3f486e4b2829c143412dc258199f181d929b25f0804083 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c380t-14088b35fb1b81d402b3f486e4b2829c143412dc258199f181d929b25f0804083 |
| container_end_page | 222 |
| container_issue | 2 |
| container_start_page | 212 |
| container_title | Technometrics |
| container_volume | 47 |
| creator | de Castro, B. Fernández Guillas, S Manteiga, W. González |
| description | In this article we give enhancements of several functional techniques to forecast sulfur dioxide levels near a power plant. The data are considered as a time series of curves. Assuming a lag-one dependence, the predictions are computed using the functional kernel (with local bandwith) and the linear autoregressive Hilbertian model. We carry out the estimation with a so-called "historical matrix," which is a subsample that emphasizes uncommon shapes. We introduce a bootstrap method to evaluate the range of the forecasts, which uses Fraiman and Muniz's order for functional data. Finally, we compare our functional techniques with neural networks and semiparametric methods, and find that the former models are often more effective. |
| doi_str_mv | 10.1198/004017005000000067 |
| format | article |
| fullrecord | <record><control><sourceid>jstor_pasca</sourceid><recordid>TN_cdi_pascalfrancis_primary_16752238</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>25470983</jstor_id><sourcerecordid>25470983</sourcerecordid><originalsourceid>FETCH-LOGICAL-c380t-14088b35fb1b81d402b3f486e4b2829c143412dc258199f181d929b25f0804083</originalsourceid><addsrcrecordid>eNp9kE9LxDAUxIMouK5-AUEIgsdqXpJukoMHXXdVWFBYPZe0TaRLt6lJq-63N0v9cxB8l3eY3wzDIHQM5BxAyQtCOAFBSEqGm4gdNIKUiYQKynbRaAskkZjso4MQVoQAo1KM0GzeN0VXuUbXeKnXbW0C1k2Jr53rQud1i63z-NGbsopY84KXfW17j28q91GVBi_Mm6nDIdqzug7m6OuP0fN89jS9SxYPt_fTq0VSMEm6BDiRMmepzSGXUHJCc2a5nBieU0lVAZxxoGVBUwlKWYiMoiqnqSUy9pdsjE6H3Na7196ELlu53sfuIaPABHDFSIToABXeheCNzVpfrbXfZECy7VrZ37Wi6ewrWYdC19brpqjCr3MiUkrZtsHJwK1C5_yPTlMuiJIs6peDXjVxuLV-d74us05vaue_Q9k_PT4BpZCDRg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>213714930</pqid></control><display><type>article</type><title>Functional Samples and Bootstrap for Predicting Sulfur Dioxide Levels</title><source>ABI/INFORM global</source><source>JSTOR Archival Journals and Primary Sources Collection</source><source>Taylor and Francis:Jisc Collections:Taylor and Francis Read and Publish Agreement 2024-2025:Science and Technology Collection (Reading list)</source><creator>de Castro, B. Fernández ; Guillas, S ; Manteiga, W. González</creator><creatorcontrib>de Castro, B. Fernández ; Guillas, S ; Manteiga, W. González</creatorcontrib><description>In this article we give enhancements of several functional techniques to forecast sulfur dioxide levels near a power plant. The data are considered as a time series of curves. Assuming a lag-one dependence, the predictions are computed using the functional kernel (with local bandwith) and the linear autoregressive Hilbertian model. We carry out the estimation with a so-called "historical matrix," which is a subsample that emphasizes uncommon shapes. We introduce a bootstrap method to evaluate the range of the forecasts, which uses Fraiman and Muniz's order for functional data. Finally, we compare our functional techniques with neural networks and semiparametric methods, and find that the former models are often more effective.</description><identifier>ISSN: 0040-1706</identifier><identifier>EISSN: 1537-2723</identifier><identifier>DOI: 10.1198/004017005000000067</identifier><identifier>CODEN: TCMTA2</identifier><language>eng</language><publisher>Alexandria, VI: Taylor & Francis</publisher><subject>Air pollutant ; Air pollution ; Artificial neural networks ; Bootstrap method ; Bootstrap methods ; Data depth ; Estimators ; Exact sciences and technology ; Forecasting models ; Functional data ; Inference from stochastic processes; time series analysis ; Mathematics ; Matrices ; Modeling ; Power plants ; Probability and statistics ; Regression analysis ; Sciences and techniques of general use ; Statistical forecasts ; Statistics ; Sulfur ; Sulfur dioxide ; Time series ; Time series forecasting ; Weather forecasting</subject><ispartof>Technometrics, 2005-05, Vol.47 (2), p.212-222</ispartof><rights>American Statistical Association and the American Society for Quality 2005</rights><rights>Copyright 2005 The American Statistical Association and The American Society for Quality</rights><rights>2005 INIST-CNRS</rights><rights>Copyright American Society for Quality May 2005</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c380t-14088b35fb1b81d402b3f486e4b2829c143412dc258199f181d929b25f0804083</citedby><cites>FETCH-LOGICAL-c380t-14088b35fb1b81d402b3f486e4b2829c143412dc258199f181d929b25f0804083</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/213714930/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/213714930?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,778,782,11671,27907,27908,36043,44346,58221,58454,74646</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=16752238$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>de Castro, B. Fernández</creatorcontrib><creatorcontrib>Guillas, S</creatorcontrib><creatorcontrib>Manteiga, W. González</creatorcontrib><title>Functional Samples and Bootstrap for Predicting Sulfur Dioxide Levels</title><title>Technometrics</title><description>In this article we give enhancements of several functional techniques to forecast sulfur dioxide levels near a power plant. The data are considered as a time series of curves. Assuming a lag-one dependence, the predictions are computed using the functional kernel (with local bandwith) and the linear autoregressive Hilbertian model. We carry out the estimation with a so-called "historical matrix," which is a subsample that emphasizes uncommon shapes. We introduce a bootstrap method to evaluate the range of the forecasts, which uses Fraiman and Muniz's order for functional data. Finally, we compare our functional techniques with neural networks and semiparametric methods, and find that the former models are often more effective.</description><subject>Air pollutant</subject><subject>Air pollution</subject><subject>Artificial neural networks</subject><subject>Bootstrap method</subject><subject>Bootstrap methods</subject><subject>Data depth</subject><subject>Estimators</subject><subject>Exact sciences and technology</subject><subject>Forecasting models</subject><subject>Functional data</subject><subject>Inference from stochastic processes; time series analysis</subject><subject>Mathematics</subject><subject>Matrices</subject><subject>Modeling</subject><subject>Power plants</subject><subject>Probability and statistics</subject><subject>Regression analysis</subject><subject>Sciences and techniques of general use</subject><subject>Statistical forecasts</subject><subject>Statistics</subject><subject>Sulfur</subject><subject>Sulfur dioxide</subject><subject>Time series</subject><subject>Time series forecasting</subject><subject>Weather forecasting</subject><issn>0040-1706</issn><issn>1537-2723</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNp9kE9LxDAUxIMouK5-AUEIgsdqXpJukoMHXXdVWFBYPZe0TaRLt6lJq-63N0v9cxB8l3eY3wzDIHQM5BxAyQtCOAFBSEqGm4gdNIKUiYQKynbRaAskkZjso4MQVoQAo1KM0GzeN0VXuUbXeKnXbW0C1k2Jr53rQud1i63z-NGbsopY84KXfW17j28q91GVBi_Mm6nDIdqzug7m6OuP0fN89jS9SxYPt_fTq0VSMEm6BDiRMmepzSGXUHJCc2a5nBieU0lVAZxxoGVBUwlKWYiMoiqnqSUy9pdsjE6H3Na7196ELlu53sfuIaPABHDFSIToABXeheCNzVpfrbXfZECy7VrZ37Wi6ewrWYdC19brpqjCr3MiUkrZtsHJwK1C5_yPTlMuiJIs6peDXjVxuLV-d74us05vaue_Q9k_PT4BpZCDRg</recordid><startdate>20050501</startdate><enddate>20050501</enddate><creator>de Castro, B. Fernández</creator><creator>Guillas, S</creator><creator>Manteiga, W. González</creator><general>Taylor & Francis</general><general>The American Society for Quality and The American Statistical Association</general><general>American Society for Quality Control</general><general>American Statistical Association</general><general>American Society for Quality</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8AO</scope><scope>8C1</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>L6V</scope><scope>M0C</scope><scope>M2P</scope><scope>M7S</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYYUZ</scope><scope>Q9U</scope><scope>S0X</scope></search><sort><creationdate>20050501</creationdate><title>Functional Samples and Bootstrap for Predicting Sulfur Dioxide Levels</title><author>de Castro, B. Fernández ; Guillas, S ; Manteiga, W. González</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c380t-14088b35fb1b81d402b3f486e4b2829c143412dc258199f181d929b25f0804083</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Air pollutant</topic><topic>Air pollution</topic><topic>Artificial neural networks</topic><topic>Bootstrap method</topic><topic>Bootstrap methods</topic><topic>Data depth</topic><topic>Estimators</topic><topic>Exact sciences and technology</topic><topic>Forecasting models</topic><topic>Functional data</topic><topic>Inference from stochastic processes; time series analysis</topic><topic>Mathematics</topic><topic>Matrices</topic><topic>Modeling</topic><topic>Power plants</topic><topic>Probability and statistics</topic><topic>Regression analysis</topic><topic>Sciences and techniques of general use</topic><topic>Statistical forecasts</topic><topic>Statistics</topic><topic>Sulfur</topic><topic>Sulfur dioxide</topic><topic>Time series</topic><topic>Time series forecasting</topic><topic>Weather forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>de Castro, B. Fernández</creatorcontrib><creatorcontrib>Guillas, S</creatorcontrib><creatorcontrib>Manteiga, W. González</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Complete</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest Public Health 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)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>ABI/INFORM global</collection><collection>ProQuest Science Journals</collection><collection>Engineering Database</collection><collection>One Business (ProQuest)</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 Central China</collection><collection>Engineering collection</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><jtitle>Technometrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>de Castro, B. Fernández</au><au>Guillas, S</au><au>Manteiga, W. González</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Functional Samples and Bootstrap for Predicting Sulfur Dioxide Levels</atitle><jtitle>Technometrics</jtitle><date>2005-05-01</date><risdate>2005</risdate><volume>47</volume><issue>2</issue><spage>212</spage><epage>222</epage><pages>212-222</pages><issn>0040-1706</issn><eissn>1537-2723</eissn><coden>TCMTA2</coden><abstract>In this article we give enhancements of several functional techniques to forecast sulfur dioxide levels near a power plant. The data are considered as a time series of curves. Assuming a lag-one dependence, the predictions are computed using the functional kernel (with local bandwith) and the linear autoregressive Hilbertian model. We carry out the estimation with a so-called "historical matrix," which is a subsample that emphasizes uncommon shapes. We introduce a bootstrap method to evaluate the range of the forecasts, which uses Fraiman and Muniz's order for functional data. Finally, we compare our functional techniques with neural networks and semiparametric methods, and find that the former models are often more effective.</abstract><cop>Alexandria, VI</cop><cop>Milwaukee, WI</cop><pub>Taylor & Francis</pub><doi>10.1198/004017005000000067</doi><tpages>11</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0040-1706 |
| ispartof | Technometrics, 2005-05, Vol.47 (2), p.212-222 |
| issn | 0040-1706 1537-2723 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_16752238 |
| source | ABI/INFORM global; JSTOR Archival Journals and Primary Sources Collection; Taylor and Francis:Jisc Collections:Taylor and Francis Read and Publish Agreement 2024-2025:Science and Technology Collection (Reading list) |
| subjects | Air pollutant Air pollution Artificial neural networks Bootstrap method Bootstrap methods Data depth Estimators Exact sciences and technology Forecasting models Functional data Inference from stochastic processes time series analysis Mathematics Matrices Modeling Power plants Probability and statistics Regression analysis Sciences and techniques of general use Statistical forecasts Statistics Sulfur Sulfur dioxide Time series Time series forecasting Weather forecasting |
| title | Functional Samples and Bootstrap for Predicting Sulfur Dioxide Levels |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-17T03%3A13%3A34IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_pasca&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Functional%20Samples%20and%20Bootstrap%20for%20Predicting%20Sulfur%20Dioxide%20Levels&rft.jtitle=Technometrics&rft.au=de%20Castro,%20B.%20Fern%C3%A1ndez&rft.date=2005-05-01&rft.volume=47&rft.issue=2&rft.spage=212&rft.epage=222&rft.pages=212-222&rft.issn=0040-1706&rft.eissn=1537-2723&rft.coden=TCMTA2&rft_id=info:doi/10.1198/004017005000000067&rft_dat=%3Cjstor_pasca%3E25470983%3C/jstor_pasca%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c380t-14088b35fb1b81d402b3f486e4b2829c143412dc258199f181d929b25f0804083%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=213714930&rft_id=info:pmid/&rft_jstor_id=25470983&rfr_iscdi=true |