TSPred: A framework for nonstationary time series prediction
The nonstationary time series prediction is challenging since it demands knowledge of both data transformation and prediction methods. This paper presents TSPred, a framework for nonstationary time series prediction. It differs from the mainstream frameworks since it establishes a prediction process...
Gespeichert in:
| Veröffentlicht in: | Neurocomputing (Amsterdam) 2022-01, Vol.467, p.197-202 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 202 |
|---|---|
| container_issue | |
| container_start_page | 197 |
| container_title | Neurocomputing (Amsterdam) |
| container_volume | 467 |
| creator | Salles, Rebecca Pacitti, Esther Bezerra, Eduardo Porto, Fabio Ogasawara, Eduardo |
| description | The nonstationary time series prediction is challenging since it demands knowledge of both data transformation and prediction methods. This paper presents TSPred, a framework for nonstationary time series prediction. It differs from the mainstream frameworks since it establishes a prediction process that seamlessly integrates nonstationary time series transformations with state-of-the-art statistical and machine learning methods. It is made available as an R-package, which provides functions for defining and conducting time series prediction, including data pre(post) processing, decomposition, modeling, prediction, and accuracy assessment. Besides, TSPred enables user-defined methods, which significantly expands the applicability of the framework. |
| doi_str_mv | 10.1016/j.neucom.2021.09.067 |
| format | Article |
| fullrecord | <record><control><sourceid>hal_cross</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_lirmm_03452170v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0925231221014454</els_id><sourcerecordid>oai_HAL_lirmm_03452170v1</sourcerecordid><originalsourceid>FETCH-LOGICAL-c388t-67ace5219b9a9ad352ae7b76de1296fb94a93027c466d20e8d283ffa8b4deffe3</originalsourceid><addsrcrecordid>eNp9kEtLAzEUhYMoWKv_wMXsZcY8pnmICKWoFQoK1nXIZG4wtTMpyVjx35sy4tLVXdxzzr3nQ-iS4Ipgwq83VQ-fNnQVxZRUWFWYiyM0IVLQUlLJj9EEKzorKSP0FJ2ltMGYCELVBN2uX18itDfFvHDRdPAV4kfhQiz60KfBDD70Jn4Xg--gSBA9pGKX9d4eNufoxJltgovfOUVvD_frxbJcPT8-Lear0jIph5ILY2FGiWqUUaZlM2pANIK3kF_grlG1UQxTYWvOW4pBtlQy54xs6hacAzZFV2Puu9nqXfRdfkkH4_VyvtJbH7tOY1bnCwLvSVbXo9rGkFIE92chWB946Y0eeekDL42Vzryy7W60QW6y9xB1sh56m8tGsINug_8_4AfDnnYX</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>TSPred: A framework for nonstationary time series prediction</title><source>Elsevier ScienceDirect Journals</source><creator>Salles, Rebecca ; Pacitti, Esther ; Bezerra, Eduardo ; Porto, Fabio ; Ogasawara, Eduardo</creator><creatorcontrib>Salles, Rebecca ; Pacitti, Esther ; Bezerra, Eduardo ; Porto, Fabio ; Ogasawara, Eduardo</creatorcontrib><description>The nonstationary time series prediction is challenging since it demands knowledge of both data transformation and prediction methods. This paper presents TSPred, a framework for nonstationary time series prediction. It differs from the mainstream frameworks since it establishes a prediction process that seamlessly integrates nonstationary time series transformations with state-of-the-art statistical and machine learning methods. It is made available as an R-package, which provides functions for defining and conducting time series prediction, including data pre(post) processing, decomposition, modeling, prediction, and accuracy assessment. Besides, TSPred enables user-defined methods, which significantly expands the applicability of the framework.</description><identifier>ISSN: 0925-2312</identifier><identifier>EISSN: 1872-8286</identifier><identifier>DOI: 10.1016/j.neucom.2021.09.067</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Computer Science ; Machine learning ; Nonstationarity ; Prediction ; Preprocessing ; Time series ; Transform</subject><ispartof>Neurocomputing (Amsterdam), 2022-01, Vol.467, p.197-202</ispartof><rights>2021 Elsevier B.V.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c388t-67ace5219b9a9ad352ae7b76de1296fb94a93027c466d20e8d283ffa8b4deffe3</citedby><cites>FETCH-LOGICAL-c388t-67ace5219b9a9ad352ae7b76de1296fb94a93027c466d20e8d283ffa8b4deffe3</cites><orcidid>0000-0002-0466-0626 ; 0000-0002-4597-4832 ; 0000-0002-1001-3839 ; 0000-0003-1370-9943</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0925231221014454$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,776,780,881,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://hal-lirmm.ccsd.cnrs.fr/lirmm-03452170$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Salles, Rebecca</creatorcontrib><creatorcontrib>Pacitti, Esther</creatorcontrib><creatorcontrib>Bezerra, Eduardo</creatorcontrib><creatorcontrib>Porto, Fabio</creatorcontrib><creatorcontrib>Ogasawara, Eduardo</creatorcontrib><title>TSPred: A framework for nonstationary time series prediction</title><title>Neurocomputing (Amsterdam)</title><description>The nonstationary time series prediction is challenging since it demands knowledge of both data transformation and prediction methods. This paper presents TSPred, a framework for nonstationary time series prediction. It differs from the mainstream frameworks since it establishes a prediction process that seamlessly integrates nonstationary time series transformations with state-of-the-art statistical and machine learning methods. It is made available as an R-package, which provides functions for defining and conducting time series prediction, including data pre(post) processing, decomposition, modeling, prediction, and accuracy assessment. Besides, TSPred enables user-defined methods, which significantly expands the applicability of the framework.</description><subject>Computer Science</subject><subject>Machine learning</subject><subject>Nonstationarity</subject><subject>Prediction</subject><subject>Preprocessing</subject><subject>Time series</subject><subject>Transform</subject><issn>0925-2312</issn><issn>1872-8286</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kEtLAzEUhYMoWKv_wMXsZcY8pnmICKWoFQoK1nXIZG4wtTMpyVjx35sy4tLVXdxzzr3nQ-iS4Ipgwq83VQ-fNnQVxZRUWFWYiyM0IVLQUlLJj9EEKzorKSP0FJ2ltMGYCELVBN2uX18itDfFvHDRdPAV4kfhQiz60KfBDD70Jn4Xg--gSBA9pGKX9d4eNufoxJltgovfOUVvD_frxbJcPT8-Lear0jIph5ILY2FGiWqUUaZlM2pANIK3kF_grlG1UQxTYWvOW4pBtlQy54xs6hacAzZFV2Puu9nqXfRdfkkH4_VyvtJbH7tOY1bnCwLvSVbXo9rGkFIE92chWB946Y0eeekDL42Vzryy7W60QW6y9xB1sh56m8tGsINug_8_4AfDnnYX</recordid><startdate>20220107</startdate><enddate>20220107</enddate><creator>Salles, Rebecca</creator><creator>Pacitti, Esther</creator><creator>Bezerra, Eduardo</creator><creator>Porto, Fabio</creator><creator>Ogasawara, Eduardo</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>AAYXX</scope><scope>CITATION</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-0466-0626</orcidid><orcidid>https://orcid.org/0000-0002-4597-4832</orcidid><orcidid>https://orcid.org/0000-0002-1001-3839</orcidid><orcidid>https://orcid.org/0000-0003-1370-9943</orcidid></search><sort><creationdate>20220107</creationdate><title>TSPred: A framework for nonstationary time series prediction</title><author>Salles, Rebecca ; Pacitti, Esther ; Bezerra, Eduardo ; Porto, Fabio ; Ogasawara, Eduardo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c388t-67ace5219b9a9ad352ae7b76de1296fb94a93027c466d20e8d283ffa8b4deffe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science</topic><topic>Machine learning</topic><topic>Nonstationarity</topic><topic>Prediction</topic><topic>Preprocessing</topic><topic>Time series</topic><topic>Transform</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Salles, Rebecca</creatorcontrib><creatorcontrib>Pacitti, Esther</creatorcontrib><creatorcontrib>Bezerra, Eduardo</creatorcontrib><creatorcontrib>Porto, Fabio</creatorcontrib><creatorcontrib>Ogasawara, Eduardo</creatorcontrib><collection>CrossRef</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Neurocomputing (Amsterdam)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Salles, Rebecca</au><au>Pacitti, Esther</au><au>Bezerra, Eduardo</au><au>Porto, Fabio</au><au>Ogasawara, Eduardo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>TSPred: A framework for nonstationary time series prediction</atitle><jtitle>Neurocomputing (Amsterdam)</jtitle><date>2022-01-07</date><risdate>2022</risdate><volume>467</volume><spage>197</spage><epage>202</epage><pages>197-202</pages><issn>0925-2312</issn><eissn>1872-8286</eissn><abstract>The nonstationary time series prediction is challenging since it demands knowledge of both data transformation and prediction methods. This paper presents TSPred, a framework for nonstationary time series prediction. It differs from the mainstream frameworks since it establishes a prediction process that seamlessly integrates nonstationary time series transformations with state-of-the-art statistical and machine learning methods. It is made available as an R-package, which provides functions for defining and conducting time series prediction, including data pre(post) processing, decomposition, modeling, prediction, and accuracy assessment. Besides, TSPred enables user-defined methods, which significantly expands the applicability of the framework.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.neucom.2021.09.067</doi><tpages>6</tpages><orcidid>https://orcid.org/0000-0002-0466-0626</orcidid><orcidid>https://orcid.org/0000-0002-4597-4832</orcidid><orcidid>https://orcid.org/0000-0002-1001-3839</orcidid><orcidid>https://orcid.org/0000-0003-1370-9943</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0925-2312 |
| ispartof | Neurocomputing (Amsterdam), 2022-01, Vol.467, p.197-202 |
| issn | 0925-2312 1872-8286 |
| language | eng |
| recordid | cdi_hal_primary_oai_HAL_lirmm_03452170v1 |
| source | Elsevier ScienceDirect Journals |
| subjects | Computer Science Machine learning Nonstationarity Prediction Preprocessing Time series Transform |
| title | TSPred: A framework for nonstationary time series prediction |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-01T16%3A37%3A44IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-hal_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=TSPred:%20A%20framework%20for%20nonstationary%20time%20series%20prediction&rft.jtitle=Neurocomputing%20(Amsterdam)&rft.au=Salles,%20Rebecca&rft.date=2022-01-07&rft.volume=467&rft.spage=197&rft.epage=202&rft.pages=197-202&rft.issn=0925-2312&rft.eissn=1872-8286&rft_id=info:doi/10.1016/j.neucom.2021.09.067&rft_dat=%3Chal_cross%3Eoai_HAL_lirmm_03452170v1%3C/hal_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_els_id=S0925231221014454&rfr_iscdi=true |