[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121525-en":3,"doc-seo-121525-105":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},121525,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Inflation Forecasting with Machine Learning - A study on the US with big data - Master Thesis","This thesis evaluates whether machine learning improves inflation forecasting accuracy. Two datasets are used: a monthly dataset widely adopted in prior studies and a weekly dataset constructed from scratch to incorporate the most recent information. Forecast performance is examined across different forecasting horizons to determine whether forecasts improve or deteriorate. The study also tests whether higher data granularity increases forecast consistency. Findings show machine learning does not outperform a strong ARIMA baseline, with Random Forest the main competitive model.","Master of Science in Business Analytics and Data Science  \nDepartment of Business Administration  \nMaster Thesis  \nInflation Forecasting with Machine Learning: A study on the  \nUS with big data  \nAuthor: Konstantinos Manetas Violetas  \nThesis Supervisor: Konstantinos Tarampanis  \nAcknowledgements  \nTo my family, whose constant support and encouragement are key determinants for every step I make.  \nTo Professor Konstantinos Tarampanis and Assistant Professor Evangelos Kalampokis, whose guidance and comments have been crucial throughout every step of the research process.  \nTo my friends and relatives, who have always believed in me.  \nTo my nephews, who are growing fast and already make their impact to the world.  \nMaster Thesis  \nInflation forecasting with Machine Learning: A study on  \nthe US with big data.  \nKonstantinos Manetas Violetas 􀀍  \nUnder the supervision of Prof. K. Tarampanis†  \nJuly 2025  \nAbstract  \nThe aim of this thesis is to investigate the extent to which Machine Learning techniques can enhance the accuracy of inflation forecasting. By using two different datasets, one with monthly data widely used in the literature and one with weekly data that I construct from scratch, I try to update the findings of the literature with the newest available data, examine how inflation forecasts for the US improve or deteriorate when using different forecasting horizonsand assess how higher data granularity can affect the consistency of the forecasts. I find that Machine Learning algorithms do not produce better forecasts than a plain ARIMA with a strong competitor being only the Random Forest model, while higher data granularity does not seem to play any role for the production of more accurate inflation forecasts.  \n􀀍 UoM, [bad24004@uom.edu.gr](bad24004@uom.edu.gr)[ ](bad24004@uom.edu.gr)† UoM, [kat@uom.edu.gr](kat@uom.edu.gr)  \nTable of Contents  \n1. Introduction..................................................................................................... 1  \n2. Literature review ............................................................................................ 3  \n3. Data .................................................................................................................. 7  \n3.1. Financial and commodity variables in inflation forecasting ............... 8  \n4. Methodologies............................................................................................... 11  \n4.1. What is Machine Learning?.................................................................. 12  \n4.2. Lasso and Ridge Regression................................................................. 13  \n4.3. Random Forest ...................................................................................... 14  \n4.4. An overview of Deep Learning and Neural Networks ..................... 15  \n4.5. ARIMA and ARIMAX models ............................................................. 19  \n5. Results............................................................................................................ 20  \n5.1. Monthly dataset .................................................................................... 21  \n5.1.1. LSTM models performance .......................................................... 21  \n5.1.2. Machine Learning and Univariate models performance ........... 22  \n5.2. Weekly dataset ...................................................................................... 32  \n5.2.1. LSTM models performance .......................................................... 32  \n5.2.2. Machine Learning and Univariate models performance ........... 37  \n6. Discussion...................................................................................................... 43  \n7. Conclusions ................................................................................................... 44  \n8. References...................................................................................................... 45  \nList of","cbCaiquDAUwycE2W","https://ap.wps.com/l/cbCaiquDAUwycE2W","pdf",2528170,1,59,"English","en",105,"# Introduction\n# Literature review\n# Data\n## Financial and commodity variables in inflation forecasting\n# Methodologies\n## What is Machine Learning?\n## Lasso and Ridge Regression\n## Random Forest\n## An overview of Deep Learning and Neural Networks\n## ARIMA and ARIMAX models\n# Results\n## Monthly dataset\n### LSTM models performance\n### Machine Learning and Univariate models performance\n## Weekly dataset\n### LSTM models performance\n### Machine Learning and Univariate models performance\n# Discussion\n# Conclusions\n# References","[{\"question\":\"What is the primary goal of the thesis?\",\"answer\":\"To assess how far machine learning techniques can enhance the accuracy of inflation forecasting.\"},{\"question\":\"How many datasets are used, and how do they differ?\",\"answer\":\"Two datasets are used: a monthly dataset from prior literature and a weekly dataset constructed from scratch using higher-frequency data.\"},{\"question\":\"Do machine learning models outperform ARIMA for US inflation forecasting?\",\"answer\":\"No. The results indicate machine learning does not produce better forecasts than a plain ARIMA baseline, with Random Forest being the strongest competitor.\"}]","Inflation Forecasting with Machine Learning - A study on the US with big data - Master Thesis | PDF",1785736090,149,{"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},"inflation-forecasting-with-machine-learning-a-study-on-the-us-with-big-data-master-thesis","",{"@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/inflation-forecasting-with-machine-learning-a-study-on-the-us-with-big-data-master-thesis/121525/",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","2026-08-03",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},"What is the primary goal of the thesis?","Question",{"text":75,"@type":76},"To assess how far machine learning techniques can enhance the accuracy of inflation forecasting.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How many datasets are used, and how do they differ?",{"text":80,"@type":76},"Two datasets are used: a monthly dataset from prior literature and a weekly dataset constructed from scratch using higher-frequency data.",{"name":82,"@type":73,"acceptedAnswer":83},"Do machine learning models outperform ARIMA for US inflation forecasting?",{"text":84,"@type":76},"No. The results indicate machine learning does not produce better forecasts than a plain ARIMA baseline, with Random Forest being the strongest competitor.","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"]