[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117844-en":3,"doc-seo-117844-105":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},117844,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Machine Learning in Inflation Prediction for the Finnish Economy","Growing inflation affects citizens worldwide, creating the need for accurate inflation prediction to support better economic decision-making. This master’s thesis advances machine learning approaches for forecasting inflation in the Finnish economy, where comparable studies are limited. The work compares machine learning models against traditional econometrics using multivariate analysis and selects the best-performing approach. It also evaluates how adding additional variables changes predictive accuracy, finding that multilayer perceptron performs best overall, while cross-validation favors support vector regression for smaller datasets and LASSO for larger ones, with most models losing accuracy when more variables are added.","Machine Learning in Inflation Prediction for the  \nFinnish Economy  \nSukrit Pant  \nMaster’s thesis in Governance of Digitalization Master’s Programme  \nSupervisor: Prof. Jozsef Mezei  \nFaculty of Social Sciences, Business and Economics, and Law  \nÅbo Akadmei University  \n\n| Subject: Information Systems |\n| --- |\n| Author: Sukrit Pant ([sukrit.pant@abo.fi](sukrit.pant@abo.fi)) |\n| Title: Machine Learning in Inflation Prediction for the Finnish Economy |\n| Language: English |\n| Supervisor: Jozsef Mezei |\n| Abstract:\u003Cbr>With growing inflation faced by countries around the world impacting the lives of citizens for each economy, there is a need for a model that can accurately predict inflation for better decision-making. Machine learning techniques have evolved in the past few years, with newer algorithms being invented and improving on previous models.\u003Cbr>Most of the research focused on larger economies such as the USA, the UK, Germany, and China. There is a demand for studies that are focused on other economies. Similar studies have not been conducted in Finland. At least those that are available for researchers and economists. The research focuses on improved accuracy of machine learning algorithms compared to traditional econometrics models. A multivariate analysis using a machine learning algorithm is performed to determine the bestperforming model for the Finnish economy.\u003Cbr>Similarly, the impact of adding further variables in inflation forecasting models is analyzed to understand the change in inflation accuracy. The study suggests that multilayer perceptron performs the best for both sets of analysis, while cross-validation results in support vector regression for smaller datasets and LASSO for larger datasets. Also, the study reveals that adding more variables impacts the accuracy negatively formost of the algorithms. |\n| Keywords: Machine learning, inflation forecast, inflation prediction |\n| Number of Pages: 74 |\n| Date: 10.05.2023 |\n\nTABLE OF CONTENTS  \n1 INTRODUCTION................................................................................................. 10  \n1.1 PROBLEM DEFINITION ...................................................................................... 10  \n1.2 OBJECTIVE OF THE STUDY................................................................................ 11  \n1.3 STRUCTURE OF THE THESIS .............................................................................. 12  \n2 LITERATURE REVIEW..................................................................................... 14  \n2.1 ECONOMIC FORECASTING AND MACHINE LEARNING ....................................... 14  \n2.2 INFLATION AND INFLATION FORECASTING ....................................................... 15  \n2.3 MACHINE LEARNING MODELS IN INFLATION FORECASTING ............................ 16  \n2.4 VARIABLE FOR MULTIVARIATE ANALYSIS IN INFLATION FORECASTING .......... 17  \n2.5 PREVIOUS STUDIES .......................................................................................... 18  \n3 RESEARCH METHODOLOGY ........................................................................25  \n3.1 RESEARCH METHOD......................................................................................... 25  \n3.2 DATA SELECTION, COLLECTION, AND CLEANING ............................................26  \n3.2.1 Data Selection ............................................................................................. 26  \n3.2.2 Data Collection ........................................................................................... 27  \n3.2.3 Data Cleaning ............................................................................................. 29  \n3.3 MODEL SELECTION .......................................................................................... 29  \n3.3.1 Decision Tree .............................................................................................. 29  \n3.3.2 Random Forest .........................................","cbCaisR2drPu3zXf","https://ap.wps.com/l/cbCaisR2drPu3zXf","pdf",821372,1,74,"English","en",105,"# Introduction\n## Problem definition\n## Objective of the study\n## Structure of the thesis\n# Literature review\n## Economic forecasting and machine learning\n## Inflation and inflation forecasting\n## Machine learning models in inflation forecasting\n## Variable for multivariate analysis in inflation forecasting\n## Previous studies\n# Research methodology\n## Research method\n## Data selection, collection, and cleaning\n## Model selection\n## Parameter tuning\n## Evaluation metrics\n## Software and library\n# Results\n## Descriptive\n## Preliminary analysis\n## Supplementary analysis\n## Comparison of optimized algorithms\n## Cross-validation\n# Discussion\n## Comparison to previous studies\n## Hyperparameter optimization","[{\"question\":\"Why is inflation prediction important in the Finnish economy context?\",\"answer\":\"Inflation influences citizens’ lives and economic outcomes, so accurate forecasting supports better decision-making. The thesis highlights limited prior work focused specifically on Finland.\"},{\"question\":\"How does the thesis evaluate machine learning performance against traditional econometrics?\",\"answer\":\"It performs multivariate analysis using machine learning algorithms and compares forecasting accuracy with traditional econometric approaches.\"},{\"question\":\"Which models perform best under different dataset sizes and variable sets?\",\"answer\":\"Multilayer perceptron performs best across the analyses. Cross-validation supports support vector regression for smaller datasets and LASSO for larger datasets, while adding more variables generally reduces accuracy for most algorithms.\"}]","Machine Learning in Inflation Prediction for the Finnish Economy | PDF",1785679961,186,{"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},"machine-learning-in-inflation-prediction-for-the-finnish-economy","",{"@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/machine-learning-in-inflation-prediction-for-the-finnish-economy/117844/",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},"Why is inflation prediction important in the Finnish economy context?","Question",{"text":75,"@type":76},"Inflation influences citizens’ lives and economic outcomes, so accurate forecasting supports better decision-making. The thesis highlights limited prior work focused specifically on Finland.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis evaluate machine learning performance against traditional econometrics?",{"text":80,"@type":76},"It performs multivariate analysis using machine learning algorithms and compares forecasting accuracy with traditional econometric approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models perform best under different dataset sizes and variable sets?",{"text":84,"@type":76},"Multilayer perceptron performs best across the analyses. Cross-validation supports support vector regression for smaller datasets and LASSO for larger datasets, while adding more variables generally reduces accuracy for most algorithms.","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"]