CPI Inflation Rate: A Comparative Analysis of Traditional Basic Structural Model (BSM) and Artificial Neural Networks (ANN) Model for Accurate Predictions in South Africa

CPI Inflation Rate: A Comparative Analysis of Traditional Basic Structural Model (BSM) and Artificial Neural Networks (ANN) Model for Accurate Predictions in South Africa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article CPI Inflation Rate: A Comparative Analysis of Traditional Basic Structural Model (BSM) and Artificial Neural Networks (ANN) Model for Accurate Predictions in South Africa Israel Maingo, Leonard Marevhula This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8633685/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Precise inflation prediction is crucial for the formulation of monetary policies and economic planning, especially in the case of developing countries like South Africa. This research investigates the predictive capabilities of conventional and machine learning models for forecasting the South African Consumer Price Index (CPI) inflation rate. The monthly values of the CPI inflation rate from January 2008 to October 2025, collected from Statistics South Africa, were considered. The models used were the Basic Structural Model (BSM), the Artificial Neural Network (ANN), and the combined hybrid BSM and ANN approach. The BSM model examines structural variables like trend and seasonality, and the model ANNs focus on nonlinearities. The model performance was assessed using the criteria of RMSE, MAE, and MASE performance measures, in the in-sample forecasting. The empirical analysis shows that the performance of the model ANN outperformed that of the BSM and hybrid BSM–ANN models considerably when compared using the in-sample forecasting, and its results were well-behaved without any features of autocorrelation and heteroscedasticity. The results showed that the combined hybrid BSM–ANN model was superior to the BSM and ANN models, although it may not necessarily outperform the other two, as the out-of-sample forecasts were more stable and reasonable. Macroeconomics Econometrics Other Economics Consumer Price Index Inflation Forecasting Basic Structural Model Artificial Neural Networks Hybrid Models Statistics South Africa Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8633685","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576408605,"identity":"5f593b92-1588-467b-a71c-1d0874d52703","order_by":0,"name":"Israel Maingo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIie2PsWrDMBCGZQqdXLqeaV+hYCg0HYz9IF0kBO6SQkcPGZRFfoUMeYWsnS8IMol6LWSplqx1oEOm0rOyZLHdMVB9g3T3cx8nMRYInCWRouPRV+u2yrp7jn9QwJcGbOmTEcVzVPBKm2MxNHtXG+2+KygmtUGEyyZf1Ya2zLKnPuXBivr+1oJY2pLja7yVb1aQsilfVJ+CQt8kGjiwaYoAW0mJwkiZfqVxXing+qvFOH2XlIwoH0Inew3RAqYMY445JWMKbWH0lwXsUgMoOSUK+dBfmuddcqgyeph0bv+TF5S4z3aW9SodF/FJI/wkHxjviA4nTTEyHAgEAv+QX9q1aC1nOmF7AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0005-2873-5636","institution":"University of Venda","correspondingAuthor":true,"prefix":"","firstName":"Israel","middleName":"","lastName":"Maingo","suffix":""},{"id":576409183,"identity":"66a6a685-2cda-4153-a8af-7cc8c30e4d01","order_by":1,"name":"Leonard Marevhula","email":"","orcid":"https://orcid.org/0009-0000-0058-0672","institution":"University of Venda","correspondingAuthor":false,"prefix":"","firstName":"Leonard","middleName":"","lastName":"Marevhula","suffix":""}],"badges":[],"createdAt":"2026-01-18 21:59:15","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8633685/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8633685/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100857957,"identity":"8fd25ec1-c997-4de5-9c31-d6d1ad76d303","added_by":"auto","created_at":"2026-01-22 07:23:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":436230,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptUpdated.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8633685/v1_covered_12e1c972-c291-4bb6-a590-77df8e70e073.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eCPI Inflation Rate: A Comparative Analysis of Traditional Basic Structural Model (BSM) and Artificial Neural Networks (ANN) Model for Accurate Predictions in South Africa\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Venda","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Consumer Price Index; Inflation Forecasting; Basic Structural Model; Artificial Neural Networks; Hybrid Models; Statistics South Africa","lastPublishedDoi":"10.21203/rs.3.rs-8633685/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8633685/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrecise inflation prediction is crucial for the formulation of monetary policies and economic planning, especially in the case of developing countries like South Africa. 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