[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120465-en":3,"doc-seo-120465-105":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":20,"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},120465,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","A comprehensive analysis of Brent Crude oil forecasting methods combining machine learning and quantitative models with application","A comprehensive analysis evaluates forecasting approaches for Brent Crude oil by integrating quantitative time-series models with modern machine learning techniques. The study defines forecasting objectives and research question, reviews relevant literature on oil price forecasting and valuation, and details a research methodology centered on time-series analysis, including AR and ARIMAX models, alongside neural network architectures such as RNN, LSTM, CNN, and GRU. Model selection, data preprocessing, assumptions, and performance evaluation guide the comparison, leading to structured results and valuation-oriented conclusions.","WISEflow Europe/0slo(CEST)  \n22 Jun 2023  \n􀀔  \nHandelsh0ysllolen Bl  \nGRA 19703 Master Thesis Thesis Master of Science 100% - W  \nPredefinert informasjon  \nStartdato:  \nSluttdato:  \nEllsamensform:  \nFlowkode:  \nIntern sensor:  \nDelta􀂓er  \nNavn:  \n09-01-2023 09:00 CET  \n03-07-2023 12:00 CESTT  \n202310ll11184IIIN00IIWIIT (Anonymisert)  \nTermin:  \nVurderingsform:  \nJon Fredrik Rasmussen Heen og Christoffer Bachke  \n202310  \nNorsk 6-trinns sllala (A-F)  \nlnformasjon fra delta􀂓er  \nTittel •: A comprehensiue analysis of Brent Crude oil forecasting methods combining machine Leaming and quantitatiue models with application  \nNaun pli ueileder •: Negar Ghanbari  \nlnneholder besuarelsen Nei konfidensielt  \nmateriale7:  \nKan besuarelsenoffentliggj•res?:  \nJa  \nGruppe  \nljruppenaun: (Anonymisert)  \nljruppenummer: 118  \nAndre medlemmer igruppen:  \nA comprehensive analysis of Brent Crude oil forecasting methods combining machine learning and quantitative models with application  \n-Master thesis  \nHand-in date:  \n03.07.2023  \nCampus:  \nBI Oslo  \nExamination code and name:  \nGRA 19703 Master Thesis  \nProgramme:  \nMaster of Science in Finance  \nSupervisor:  \nNegar Ghanbari  \nCandidates:  \nChristoffer Bachke & Jon Fredrik Heen  \nThis thesis was written as a part of the Master of Science in Finance at BI Norwegian Business School. The school takes no responsibility for the methods used, results found, or conclusions drawn.  \nContent  \nABSTRACT...............................................................................................................................III  \nACKNOWLEDGEMENT ....................................................................................................IV  \nLIST OF ABBREVIATIONS .............................................................................................. V  \n1. INTRODUCTION ........................................................................................................... 1  \n1.1 PURPOSE ............................................................................................................................ 1  \n1.2 MOTIVATION ..................................................................................................................... 2  \n1.3 RESEARCH QUESTION ....................................................................................................... 3  \n2. LITERATURE REVIEW ............................................................................................. 3  \n2.1 FORECASTING OIL PRICE .................................................................................................. 3  \n2.2 VALUATION........................................................................................................................ 6  \n3. RESEARCH METHODOLOGY ................................................................................ 7  \n3.1 TIME SERIES ANALYSIS .................................................................................................... 7  \n3.1.1 The Autoregressive model (AR) ........................................................................... 7  \n3.1.2 The ARIMAX model .............................................................................................. 8  \n3.2 MACHINE LEARNING ......................................................................................................... 9  \n3.2.1 Recurring Neural Network (RNN) ....................................................................... 9  \n3.2.2 Long Short-Term Memory (LSTM) ................................................................... 10  \n3.2.3 Convolutional Neural Network (CNN) .............................................................. 11  \n3.2.4 Gated Recurrent Unit (GRU) ............................................................................. 11  \n3.3 FORECAST PERFORMANCE ............................................................................................. 12  \n3.4 VALUATION METHODS ......................................................","cbCaieTYb2NxYQlc","https://ap.wps.com/l/cbCaieTYb2NxYQlc","pdf",1450651,1,85,"English","en",105,"# Abstract\n# Acknowledgement\n# List of Abbreviations\n# Introduction\n## Purpose\n## Motivation\n## Research Question\n# Literature Review\n## Forecasting Oil Price\n## Valuation\n# Research Methodology\n## Time Series Analysis\n## Machine Learning\n## Forecast Performance\n## Valuation Methods\n# Data Collection\n## Preprocessing and Splitting the Data\n## Accounting Data\n## Assumptions and Limitations\n# Model Selection\n## ARIMAX\n## Machine Learning\n## Results and Analysis","[{\"question\":\"What is the main purpose of the thesis regarding Brent Crude oil forecasting?\",\"answer\":\"The thesis aims to comprehensively analyze Brent Crude oil forecasting methods by combining quantitative models with machine learning approaches and applying them to forecasting tasks.\"},{\"question\":\"Which forecasting model families are compared in the research methodology?\",\"answer\":\"The methodology covers time-series models such as AR and ARIMAX, and machine learning models including RNN, LSTM, CNN, and GRU.\"},{\"question\":\"How are models evaluated and valued in the study?\",\"answer\":\"The thesis includes a dedicated section for forecast performance and uses valuation methods such as present value approaches and the Black-Scholes model to assess implications.\"}]","A comprehensive analysis of Brent Crude oil forecasting methods combining machine learning and quantitative models with application | 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