An improved monarch butterfly optimization based multivariate fuzzy time series approach for forecasting GDP of India
Gross Domestic Product (GDP) is a crucial indicator to evaluate national economic development of a nation and the status of the macro-economy of a country. In the present work, we have proposed a novel approach for predicting India’s nominal GDP. Six new variables have been considered to predict the...
Gespeichert in:
Veröffentlicht in: | Evolutionary intelligence 2023-04, Vol.16 (2), p.605-619 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 619 |
---|---|
container_issue | 2 |
container_start_page | 605 |
container_title | Evolutionary intelligence |
container_volume | 16 |
creator | Jha, Vijayendra Vishal Jajoo, Kanushree Sandeep Tripathy, B. K. Saleem Durai, M. A. |
description | Gross Domestic Product (GDP) is a crucial indicator to evaluate national economic development of a nation and the status of the macro-economy of a country. In the present work, we have proposed a novel approach for predicting India’s nominal GDP. Six new variables have been considered to predict the GDP of India for which a hybridised model comprising of the Multivariate Fuzzy Time Series (MVFTS) model and the Monarch Butterfly Optimization (MBO) algorithm is used. MBO is used to determine the optimal length of intervals in the Universe of Discourse (UoD) while keeping the number of intervals constant. The accuracy of the resulting algorithm is determined by taking the measures, Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The outcome obtained shows that the proposed MVFTS-MBO algorithm outperforms the existing methods for the prediction of India’s GDP. |
doi_str_mv | 10.1007/s12065-021-00686-8 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2789765115</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2789765115</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-150ae8a157b6e68904e316a4f9340f4676bf5b45dbeb8a689b6b97422793b60d3</originalsourceid><addsrcrecordid>eNp9kL1OwzAURi0EEqXwAkyWmAN24t-xKlAqIcEAs2UndnHVJMV2KrVPj0sQbAzW9XC-c68-AK4xusUI8buIS8RogUpcIMQEK8QJmGDBSEEl5qe_fyTPwUWM6wyViJMJGGYd9O029DvbwLbvdKg_oBlSssFt9rDfJt_6g06-76DR8QgNm-R3OnidLHTD4bCHmbEw2uBthHqbZTpLXB-Oz9Y6Jt-t4OL-FfYOLrvG60tw5vQm2qufOQXvjw9v86fi-WWxnM-ei7rCMhWYIm2FxpQbZpmQiNgKM02crAhyhHFmHDWENsYaoTNgmJGclCWXlWGoqabgZvTmmz4HG5Na90Po8kpVciE5oxjTTJUjVYc-xmCd2gbf6rBXGKljvWqsV-V61Xe9SuRQNYZihruVDX_qf1JfUcd-7w</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2789765115</pqid></control><display><type>article</type><title>An improved monarch butterfly optimization based multivariate fuzzy time series approach for forecasting GDP of India</title><source>SpringerLink Journals</source><creator>Jha, Vijayendra Vishal ; Jajoo, Kanushree Sandeep ; Tripathy, B. K. ; Saleem Durai, M. A.</creator><creatorcontrib>Jha, Vijayendra Vishal ; Jajoo, Kanushree Sandeep ; Tripathy, B. K. ; Saleem Durai, M. A.</creatorcontrib><description>Gross Domestic Product (GDP) is a crucial indicator to evaluate national economic development of a nation and the status of the macro-economy of a country. In the present work, we have proposed a novel approach for predicting India’s nominal GDP. Six new variables have been considered to predict the GDP of India for which a hybridised model comprising of the Multivariate Fuzzy Time Series (MVFTS) model and the Monarch Butterfly Optimization (MBO) algorithm is used. MBO is used to determine the optimal length of intervals in the Universe of Discourse (UoD) while keeping the number of intervals constant. The accuracy of the resulting algorithm is determined by taking the measures, Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The outcome obtained shows that the proposed MVFTS-MBO algorithm outperforms the existing methods for the prediction of India’s GDP.</description><identifier>ISSN: 1864-5909</identifier><identifier>EISSN: 1864-5917</identifier><identifier>DOI: 10.1007/s12065-021-00686-8</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Applications of Mathematics ; Artificial Intelligence ; Bioinformatics ; Control ; Economic development ; Engineering ; Error analysis ; Intervals ; Mathematical and Computational Engineering ; Mechatronics ; Multivariate analysis ; Optimization ; Research Paper ; Robotics ; Root-mean-square errors ; Statistical Physics and Dynamical Systems ; Time series</subject><ispartof>Evolutionary intelligence, 2023-04, Vol.16 (2), p.605-619</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-150ae8a157b6e68904e316a4f9340f4676bf5b45dbeb8a689b6b97422793b60d3</citedby><cites>FETCH-LOGICAL-c319t-150ae8a157b6e68904e316a4f9340f4676bf5b45dbeb8a689b6b97422793b60d3</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/s12065-021-00686-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12065-021-00686-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Jha, Vijayendra Vishal</creatorcontrib><creatorcontrib>Jajoo, Kanushree Sandeep</creatorcontrib><creatorcontrib>Tripathy, B. K.</creatorcontrib><creatorcontrib>Saleem Durai, M. A.</creatorcontrib><title>An improved monarch butterfly optimization based multivariate fuzzy time series approach for forecasting GDP of India</title><title>Evolutionary intelligence</title><addtitle>Evol. Intel</addtitle><description>Gross Domestic Product (GDP) is a crucial indicator to evaluate national economic development of a nation and the status of the macro-economy of a country. In the present work, we have proposed a novel approach for predicting India’s nominal GDP. Six new variables have been considered to predict the GDP of India for which a hybridised model comprising of the Multivariate Fuzzy Time Series (MVFTS) model and the Monarch Butterfly Optimization (MBO) algorithm is used. MBO is used to determine the optimal length of intervals in the Universe of Discourse (UoD) while keeping the number of intervals constant. The accuracy of the resulting algorithm is determined by taking the measures, Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The outcome obtained shows that the proposed MVFTS-MBO algorithm outperforms the existing methods for the prediction of India’s GDP.</description><subject>Algorithms</subject><subject>Applications of Mathematics</subject><subject>Artificial Intelligence</subject><subject>Bioinformatics</subject><subject>Control</subject><subject>Economic development</subject><subject>Engineering</subject><subject>Error analysis</subject><subject>Intervals</subject><subject>Mathematical and Computational Engineering</subject><subject>Mechatronics</subject><subject>Multivariate analysis</subject><subject>Optimization</subject><subject>Research Paper</subject><subject>Robotics</subject><subject>Root-mean-square errors</subject><subject>Statistical Physics and Dynamical Systems</subject><subject>Time series</subject><issn>1864-5909</issn><issn>1864-5917</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kL1OwzAURi0EEqXwAkyWmAN24t-xKlAqIcEAs2UndnHVJMV2KrVPj0sQbAzW9XC-c68-AK4xusUI8buIS8RogUpcIMQEK8QJmGDBSEEl5qe_fyTPwUWM6wyViJMJGGYd9O029DvbwLbvdKg_oBlSssFt9rDfJt_6g06-76DR8QgNm-R3OnidLHTD4bCHmbEw2uBthHqbZTpLXB-Oz9Y6Jt-t4OL-FfYOLrvG60tw5vQm2qufOQXvjw9v86fi-WWxnM-ei7rCMhWYIm2FxpQbZpmQiNgKM02crAhyhHFmHDWENsYaoTNgmJGclCWXlWGoqabgZvTmmz4HG5Na90Po8kpVciE5oxjTTJUjVYc-xmCd2gbf6rBXGKljvWqsV-V61Xe9SuRQNYZihruVDX_qf1JfUcd-7w</recordid><startdate>20230401</startdate><enddate>20230401</enddate><creator>Jha, Vijayendra Vishal</creator><creator>Jajoo, Kanushree Sandeep</creator><creator>Tripathy, B. K.</creator><creator>Saleem Durai, M. A.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20230401</creationdate><title>An improved monarch butterfly optimization based multivariate fuzzy time series approach for forecasting GDP of India</title><author>Jha, Vijayendra Vishal ; Jajoo, Kanushree Sandeep ; Tripathy, B. K. ; Saleem Durai, M. A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-150ae8a157b6e68904e316a4f9340f4676bf5b45dbeb8a689b6b97422793b60d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Applications of Mathematics</topic><topic>Artificial Intelligence</topic><topic>Bioinformatics</topic><topic>Control</topic><topic>Economic development</topic><topic>Engineering</topic><topic>Error analysis</topic><topic>Intervals</topic><topic>Mathematical and Computational Engineering</topic><topic>Mechatronics</topic><topic>Multivariate analysis</topic><topic>Optimization</topic><topic>Research Paper</topic><topic>Robotics</topic><topic>Root-mean-square errors</topic><topic>Statistical Physics and Dynamical Systems</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jha, Vijayendra Vishal</creatorcontrib><creatorcontrib>Jajoo, Kanushree Sandeep</creatorcontrib><creatorcontrib>Tripathy, B. K.</creatorcontrib><creatorcontrib>Saleem Durai, M. A.</creatorcontrib><collection>CrossRef</collection><jtitle>Evolutionary intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jha, Vijayendra Vishal</au><au>Jajoo, Kanushree Sandeep</au><au>Tripathy, B. K.</au><au>Saleem Durai, M. A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An improved monarch butterfly optimization based multivariate fuzzy time series approach for forecasting GDP of India</atitle><jtitle>Evolutionary intelligence</jtitle><stitle>Evol. Intel</stitle><date>2023-04-01</date><risdate>2023</risdate><volume>16</volume><issue>2</issue><spage>605</spage><epage>619</epage><pages>605-619</pages><issn>1864-5909</issn><eissn>1864-5917</eissn><abstract>Gross Domestic Product (GDP) is a crucial indicator to evaluate national economic development of a nation and the status of the macro-economy of a country. In the present work, we have proposed a novel approach for predicting India’s nominal GDP. Six new variables have been considered to predict the GDP of India for which a hybridised model comprising of the Multivariate Fuzzy Time Series (MVFTS) model and the Monarch Butterfly Optimization (MBO) algorithm is used. MBO is used to determine the optimal length of intervals in the Universe of Discourse (UoD) while keeping the number of intervals constant. The accuracy of the resulting algorithm is determined by taking the measures, Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The outcome obtained shows that the proposed MVFTS-MBO algorithm outperforms the existing methods for the prediction of India’s GDP.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12065-021-00686-8</doi><tpages>15</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1864-5909 |
ispartof | Evolutionary intelligence, 2023-04, Vol.16 (2), p.605-619 |
issn | 1864-5909 1864-5917 |
language | eng |
recordid | cdi_proquest_journals_2789765115 |
source | SpringerLink Journals |
subjects | Algorithms Applications of Mathematics Artificial Intelligence Bioinformatics Control Economic development Engineering Error analysis Intervals Mathematical and Computational Engineering Mechatronics Multivariate analysis Optimization Research Paper Robotics Root-mean-square errors Statistical Physics and Dynamical Systems Time series |
title | An improved monarch butterfly optimization based multivariate fuzzy time series approach for forecasting GDP of India |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T02%3A56%3A04IST&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=An%20improved%20monarch%20butterfly%20optimization%20based%20multivariate%20fuzzy%20time%20series%20approach%20for%20forecasting%20GDP%20of%20India&rft.jtitle=Evolutionary%20intelligence&rft.au=Jha,%20Vijayendra%20Vishal&rft.date=2023-04-01&rft.volume=16&rft.issue=2&rft.spage=605&rft.epage=619&rft.pages=605-619&rft.issn=1864-5909&rft.eissn=1864-5917&rft_id=info:doi/10.1007/s12065-021-00686-8&rft_dat=%3Cproquest_cross%3E2789765115%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=2789765115&rft_id=info:pmid/&rfr_iscdi=true |