[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118335-en":3,"doc-seo-118335-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118335,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Forecasting Inflation - Can Machine Learning Outperform Econometric Models?","This thesis investigates the comparative effectiveness of machine learning algorithms versus traditional econometric models for forecasting the Consumer Price Index (CPI) as an inflation indicator. Accurate inflation forecasts are essential for central banks and policymakers to design effective monetary policy. While econometric approaches like ARIMA are widely used, advances in computation and richer data have enabled machine learning to potentially deliver stronger predictive performance. The study compares ARIMA and a random forest model using RMSE and MAE.","Master Degree Program in  \nData Science and Advanced Analytics  \nMDSAA  \nFORECASTING INFLATION  \nCAN MACHINE LEARNING OUTPERFOM ECONOMETRIC MODELS?  \nMargarida Couceiro Feio de Almeida Ferreira  \nMaster Thesis  \npresented as partial requirement for obtaining a Master’s Degree in Data Science and Advanced Analytics  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nNOVA Information Management School  \nInstituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nFORECASTING INFLATION  \nCAN MACHINE LEARNING OUTPERFOM ECONOMETRIC MODELS?  \nby  \nMargarida Couceiro Feio de Almeida Ferreira  \nMaster Thesis presented as partial requirement for obtaining the Master’s degree in Data Science and Advanced Analytics, with a specialization in Business Analytics  \nSupervised by  \nProf. Dr. Jorge Miguel Ventura Bravo, PhD  \nNOVA IMS & & Université Paris-Dauphine PSL  \nSTATEMENT OF INTEGRITY  \nI hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Rules of Conduct and Code of Honor from the NOVA Information Management School.  \nLisbon, 15th of July 2024  \nDEDICATION  \nTo my grandparents, José Carlos Alves D’Almeida and Maria Júlia Couceiro Feio Fernandes de Almeida, for always being an inspiration to me and for being the best people I have ever  \nknown.  \nACKNOWLEDGEMENTS  \nI would like to express my sincere gratitude to my supervisor, Jorge Miguel Ventura Bravo, for his guidance and support throughout this process. Your expertise and insights have been invaluable and enhanced the quality of this work.  \nI extend my deepest gratitude to my mother, Isabel Margarida de Almeida, and my brother, Rodrigo Luís Ferreira, for their unwavering love, support and encouragement in both my academic and personal life. Your belief in me has been a constant source of motivation and strength. I am profoundly thankful.  \nI am also deeply grateful to my family, specialy to my aunt, Helena Alexandra Penalva, and my uncle, Pedro Miguel Penalva, for always giving me motivation and assistance when necessary.  \nFinally, I would like to thank my friends, who have always been by my side and helped me along the way. Your support has meant the world to me.  \nABSTRACT  \nThis thesis investigates the comparative efficacy of machine learning algorithms versus traditional econometric models in forecasting the Consumer Price Index (CPI) as an indicator of inflation. Accurate inflation prediction is crucial for central banks and policymakers to devise effective monetary policies. To develop efficient monetary policies, central banks and policymakers must be able to accurately anticipate inflation. While traditional econometric models such as ARIMA have been widely utilised for inflation forecasting, the introduction of advanced computational methods and enhanced data availability has opened new avenues for machine learning models to potentially provide superior predictive skills. In this study, both the ARIMA and the random forest models are used to forecast the CPI, with their performance being assessed through standard metrics that include root mean squared error (RMSE) and mean absolute error (MAE) . Empirical results indicate that the random forest model significantly outperforms the ARIMA model on short-, medium-, and long-term forecasting horizons. Specifically, the random forest model exhibits lower RMSE and MAE values, which signifies greater predictive accuracy and reliability. These findings suggest that machine learning models have significant potential for improving inflation forecast accuracy providing valuable insights for economic policy formulation.  \nKEYWORDS  \nMachine Learning; Random Forest; ARIMA; Consumer Price Index; Time Series  \nRESUM","cbCaitzLHbCz2uTk","https://ap.wps.com/l/cbCaitzLHbCz2uTk","pdf",1160065,1,64,"English","en",105,"# 1. Introduction\n# 2. Literature review\n## 2.1. Background\n## 2.2. Related Work","[{\"question\":\"Which models are compared for CPI inflation forecasting?\",\"answer\":\"The thesis compares ARIMA with a random forest model for forecasting the Consumer Price Index (CPI).\"},{\"question\":\"How is forecasting performance evaluated in the study?\",\"answer\":\"Performance is assessed using standard metrics including root mean squared error (RMSE) and mean absolute error (MAE).\"},{\"question\":\"What is the main finding about machine learning versus econometric models?\",\"answer\":\"Results indicate that the random forest model significantly outperforms ARIMA across short-, medium-, and long-term forecasting horizons, showing lower RMSE and MAE.\"}]","Forecasting Inflation - Can Machine Learning Outperform Econometric Models? | PDF",1785683129,161,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"forecasting-inflation-can-machine-learning-outperform-econometric-models","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/forecasting-inflation-can-machine-learning-outperform-econometric-models/118335/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which models are compared for CPI inflation forecasting?","Question",{"text":75,"@type":76},"The thesis compares ARIMA with a random forest model for forecasting the Consumer Price Index (CPI).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is forecasting performance evaluated in the study?",{"text":80,"@type":76},"Performance is assessed using standard metrics including root mean squared error (RMSE) and mean absolute error (MAE).",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main finding about machine learning versus econometric models?",{"text":84,"@type":76},"Results indicate that the random forest model significantly outperforms ARIMA across short-, medium-, and long-term forecasting horizons, showing lower RMSE and MAE.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]