Forecasting time series data containing outliers with the ARIMA additive outlier method
ARIMA method is often used in time series data forecasting. However, when an outlier occurs or there is an observation value that is far from a set of data, forecasting with this method will provide a large residual so that the normality assumption is not met. One method developed from the ARIMA Met...
Gespeichert in:
| Veröffentlicht in: | Journal of physics. Conference series 2021-05, Vol.1899 (1), p.12106 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 1 |
| container_start_page | 12106 |
| container_title | Journal of physics. Conference series |
| container_volume | 1899 |
| creator | Laome, Lilis Adhi Wibawa, Gusti Ngurah Raya, Rasas Makkulau Asbahuna, Abdul Rahman |
| description | ARIMA method is often used in time series data forecasting. However, when an outlier occurs or there is an observation value that is far from a set of data, forecasting with this method will provide a large residual so that the normality assumption is not met. One method developed from the ARIMA Method that overcomes outliers, is called the ARIMA Additive Outlier Method (ARIMA AO). The purpose of this study is to forecast time series data containing outliers with the ARIMA AO Method. This method can reduce the presence of outliers with iterative procedures. The data used is data on the number of foreign tourists visiting the Port of Tanjung Priok. The results obtained are that the model has a normal distribution residual and forecasting accuracy value with MSE and MAPE is smaller than the ARIMA. |
| doi_str_mv | 10.1088/1742-6596/1899/1/012106 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_iop_j</sourceid><recordid>TN_cdi_proquest_journals_2535631300</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2535631300</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2746-c017a385407d30cda1f9811bf37dba5c48c11bb7c3ed4e8949f2ea68274d7fdf3</originalsourceid><addsrcrecordid>eNqFkF1LwzAUhoMoOKe_wYB3Qm3StE16OYabk4niB16GLEldxtbUJFP896ZUJ4JgbpJw3ucczgPAKUYXGDGWYppnSVlUZYpZVaU4RTjDqNwDg11lf_dm7BAceb9CiMRDB-B5Yp2WwgfTvMBgNhp67Yz2UIkgoLRNEKbpanYb1kY7D99NWMKw1HB0P7sZQaGUCeZNfwfgRoelVcfgoBZrr0--7iF4mlw-jq-S-e10Nh7NE5nRvEwkwlQQVuSIKoKkEriuGMaLmlC1EIXMmYy_BZVEq1yzKq_qTIuSRVjRWtVkCM76vq2zr1vtA1_ZrWviSJ4VpCgJJnHXIaB9SjrrvdM1b53ZCPfBMeKdRd754Z0r3lnkmPcWI0l60tj2p_X_1Pkf1PXd-OF3kLdxhU8mC4Ir</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2535631300</pqid></control><display><type>article</type><title>Forecasting time series data containing outliers with the ARIMA additive outlier method</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Institute of Physics Open Access Journal Titles</source><source>IOPscience extra</source><source>Alma/SFX Local Collection</source><source>Free Full-Text Journals in Chemistry</source><creator>Laome, Lilis ; Adhi Wibawa, Gusti Ngurah ; Raya, Rasas ; Makkulau ; Asbahuna, Abdul Rahman</creator><creatorcontrib>Laome, Lilis ; Adhi Wibawa, Gusti Ngurah ; Raya, Rasas ; Makkulau ; Asbahuna, Abdul Rahman</creatorcontrib><description>ARIMA method is often used in time series data forecasting. However, when an outlier occurs or there is an observation value that is far from a set of data, forecasting with this method will provide a large residual so that the normality assumption is not met. One method developed from the ARIMA Method that overcomes outliers, is called the ARIMA Additive Outlier Method (ARIMA AO). The purpose of this study is to forecast time series data containing outliers with the ARIMA AO Method. This method can reduce the presence of outliers with iterative procedures. The data used is data on the number of foreign tourists visiting the Port of Tanjung Priok. The results obtained are that the model has a normal distribution residual and forecasting accuracy value with MSE and MAPE is smaller than the ARIMA.</description><identifier>ISSN: 1742-6588</identifier><identifier>EISSN: 1742-6596</identifier><identifier>DOI: 10.1088/1742-6596/1899/1/012106</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Autoregressive models ; Forecasting ; Iterative methods ; Normal distribution ; Outliers (statistics) ; Physics ; Statistical analysis ; Time series</subject><ispartof>Journal of physics. Conference series, 2021-05, Vol.1899 (1), p.12106</ispartof><rights>Published under licence by IOP Publishing Ltd</rights><rights>2021. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2746-c017a385407d30cda1f9811bf37dba5c48c11bb7c3ed4e8949f2ea68274d7fdf3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://iopscience.iop.org/article/10.1088/1742-6596/1899/1/012106/pdf$$EPDF$$P50$$Giop$$Hfree_for_read</linktopdf><link.rule.ids>314,778,782,27911,27912,38855,38877,53827,53854</link.rule.ids></links><search><creatorcontrib>Laome, Lilis</creatorcontrib><creatorcontrib>Adhi Wibawa, Gusti Ngurah</creatorcontrib><creatorcontrib>Raya, Rasas</creatorcontrib><creatorcontrib>Makkulau</creatorcontrib><creatorcontrib>Asbahuna, Abdul Rahman</creatorcontrib><title>Forecasting time series data containing outliers with the ARIMA additive outlier method</title><title>Journal of physics. Conference series</title><addtitle>J. Phys.: Conf. Ser</addtitle><description>ARIMA method is often used in time series data forecasting. However, when an outlier occurs or there is an observation value that is far from a set of data, forecasting with this method will provide a large residual so that the normality assumption is not met. One method developed from the ARIMA Method that overcomes outliers, is called the ARIMA Additive Outlier Method (ARIMA AO). The purpose of this study is to forecast time series data containing outliers with the ARIMA AO Method. This method can reduce the presence of outliers with iterative procedures. The data used is data on the number of foreign tourists visiting the Port of Tanjung Priok. The results obtained are that the model has a normal distribution residual and forecasting accuracy value with MSE and MAPE is smaller than the ARIMA.</description><subject>Autoregressive models</subject><subject>Forecasting</subject><subject>Iterative methods</subject><subject>Normal distribution</subject><subject>Outliers (statistics)</subject><subject>Physics</subject><subject>Statistical analysis</subject><subject>Time series</subject><issn>1742-6588</issn><issn>1742-6596</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqFkF1LwzAUhoMoOKe_wYB3Qm3StE16OYabk4niB16GLEldxtbUJFP896ZUJ4JgbpJw3ucczgPAKUYXGDGWYppnSVlUZYpZVaU4RTjDqNwDg11lf_dm7BAceb9CiMRDB-B5Yp2WwgfTvMBgNhp67Yz2UIkgoLRNEKbpanYb1kY7D99NWMKw1HB0P7sZQaGUCeZNfwfgRoelVcfgoBZrr0--7iF4mlw-jq-S-e10Nh7NE5nRvEwkwlQQVuSIKoKkEriuGMaLmlC1EIXMmYy_BZVEq1yzKq_qTIuSRVjRWtVkCM76vq2zr1vtA1_ZrWviSJ4VpCgJJnHXIaB9SjrrvdM1b53ZCPfBMeKdRd754Z0r3lnkmPcWI0l60tj2p_X_1Pkf1PXd-OF3kLdxhU8mC4Ir</recordid><startdate>20210501</startdate><enddate>20210501</enddate><creator>Laome, Lilis</creator><creator>Adhi Wibawa, Gusti Ngurah</creator><creator>Raya, Rasas</creator><creator>Makkulau</creator><creator>Asbahuna, Abdul Rahman</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20210501</creationdate><title>Forecasting time series data containing outliers with the ARIMA additive outlier method</title><author>Laome, Lilis ; Adhi Wibawa, Gusti Ngurah ; Raya, Rasas ; Makkulau ; Asbahuna, Abdul Rahman</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2746-c017a385407d30cda1f9811bf37dba5c48c11bb7c3ed4e8949f2ea68274d7fdf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Autoregressive models</topic><topic>Forecasting</topic><topic>Iterative methods</topic><topic>Normal distribution</topic><topic>Outliers (statistics)</topic><topic>Physics</topic><topic>Statistical analysis</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Laome, Lilis</creatorcontrib><creatorcontrib>Adhi Wibawa, Gusti Ngurah</creatorcontrib><creatorcontrib>Raya, Rasas</creatorcontrib><creatorcontrib>Makkulau</creatorcontrib><creatorcontrib>Asbahuna, Abdul Rahman</creatorcontrib><collection>Institute of Physics Open Access Journal Titles</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</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><jtitle>Journal of physics. Conference series</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Laome, Lilis</au><au>Adhi Wibawa, Gusti Ngurah</au><au>Raya, Rasas</au><au>Makkulau</au><au>Asbahuna, Abdul Rahman</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Forecasting time series data containing outliers with the ARIMA additive outlier method</atitle><jtitle>Journal of physics. Conference series</jtitle><addtitle>J. Phys.: Conf. Ser</addtitle><date>2021-05-01</date><risdate>2021</risdate><volume>1899</volume><issue>1</issue><spage>12106</spage><pages>12106-</pages><issn>1742-6588</issn><eissn>1742-6596</eissn><abstract>ARIMA method is often used in time series data forecasting. However, when an outlier occurs or there is an observation value that is far from a set of data, forecasting with this method will provide a large residual so that the normality assumption is not met. One method developed from the ARIMA Method that overcomes outliers, is called the ARIMA Additive Outlier Method (ARIMA AO). The purpose of this study is to forecast time series data containing outliers with the ARIMA AO Method. This method can reduce the presence of outliers with iterative procedures. The data used is data on the number of foreign tourists visiting the Port of Tanjung Priok. The results obtained are that the model has a normal distribution residual and forecasting accuracy value with MSE and MAPE is smaller than the ARIMA.</abstract><cop>Bristol</cop><pub>IOP Publishing</pub><doi>10.1088/1742-6596/1899/1/012106</doi><tpages>5</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1742-6588 |
| ispartof | Journal of physics. Conference series, 2021-05, Vol.1899 (1), p.12106 |
| issn | 1742-6588 1742-6596 |
| language | eng |
| recordid | cdi_proquest_journals_2535631300 |
| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Institute of Physics Open Access Journal Titles; IOPscience extra; Alma/SFX Local Collection; Free Full-Text Journals in Chemistry |
| subjects | Autoregressive models Forecasting Iterative methods Normal distribution Outliers (statistics) Physics Statistical analysis Time series |
| title | Forecasting time series data containing outliers with the ARIMA additive outlier method |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T14%3A03%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_iop_j&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Forecasting%20time%20series%20data%20containing%20outliers%20with%20the%20ARIMA%20additive%20outlier%20method&rft.jtitle=Journal%20of%20physics.%20Conference%20series&rft.au=Laome,%20Lilis&rft.date=2021-05-01&rft.volume=1899&rft.issue=1&rft.spage=12106&rft.pages=12106-&rft.issn=1742-6588&rft.eissn=1742-6596&rft_id=info:doi/10.1088/1742-6596/1899/1/012106&rft_dat=%3Cproquest_iop_j%3E2535631300%3C/proquest_iop_j%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2535631300&rft_id=info:pmid/&rfr_iscdi=true |