[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125440-en":3,"doc-seo-125440-105":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},125440,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Comparative Analysis of Machine Learning Models for Time Series Sales Forecasting in Supply Chain Management - Master Thesis","Accurate sales forecasting in supply chain management is a critical challenge affecting inventory control, logistics efficiency, and strategic planning. This thesis evaluates how classic statistical, hybrid, and machine learning approaches predict actual seasonal sales data using lagged sales, price, discount, and time-based indicators across 96 products over 33 months. Four models—Pooled OLS, Prophet, Random Forest, and XGBoost—are trained and validated under a unified framework using RMSE, MAE, MAPE, and R² metrics. Results show Random Forest delivers the best accuracy and lowest errors, while Prophet captures seasonality but struggles with promotion-driven volatility, guiding practical model selection.","UNIVERSITA’ DEGLI STUDI DI PADOVA  \nDIPARTIMENTO DI SCIENZE ECONOMICHE EDAZIENDALI \u003CM. FANNO=  \nMaster Thesis in Business Administration  \nCOMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR TIME SERIES SALES FORECASTING IN SUPPLY CHAIN MANAGEMENT  \nSupervisor  \nProf. Enrico Rettore University of Padova  \nMaster Candidate  \nGhazal Beignezhad 2088672  \nACADEMIC YEAR  \nIl candidato dichiara che il presente lavoro è originale e non è già stato sottoposto, in tutto o in parte, per il conseguimento di un titolo accademico in altre Università italiane o straniere.  \nIl candidato dichiara altresì che tutti i materiali utilizzati durante la preparazione dell’elaboratosono stati indicati nel testo e nella sezione \u003CRiferimenti bibliogra昀椀ci= e che le eventuali citazionitestuali sono individuabili attraverso l’esplicito richiamo alla pubblicazione originale.  \nThe candidate declares that the present work is original and has not already been submitted, totally or in part, for the purposes of attaining an academic degree in other Italian or foreign universities. The candidate also declares that all the materials used during the preparation of the thesis have been explicitly indicated in the text and in the section \"Bibliographical references\" and that any textual citations can be identi昀椀ed through an explicit reference to the original publication.  \nFirma dello studente  \n\u003CTo the brave women of Iran4  \nwho continue to 昀椀ght for their basic human rights every day yet still shine brilliantly and break the chains that are made to stop them. Your resilience and courage are an inspiration. \u003CWomen,  \nLife, Freedom= To my country4  \nNow caught in the cross昀椀re of the Islamic Republic and Israel, may the day come when we are no longer silenced or subdued. When we are free to live as our true selves, to choose our paths without coercion, to raise our voices without fear. May peace replace oppression, and dignity return to every home. Until then, we remember, we resist, and we hope.  \nTo my parents4  \nwho stayed behind so I could move forward, who sacri昀椀ced not just time but presence, watching from afar as I chased my dreams. Your unwavering love, quiet strength, and boundless support have been the foundation beneath my every step. This achievement is as much yours as it is mine.  \nTo Helia, Fatemeh and Bahar4my chosen family, my relentless cheerleaders.  \nFor every time you lifted me up when I felt like falling, for the patience with which you endured my exhaustion and frustration4I am endlessly grateful. You stayed up with me on deadline nights, reminding me that I was not alone. You encouraged me when I doubted myself, believed in me when I struggled to, and celebrated even my smallest victories. You carried me through this, in ways both big and small, and I will never forget it.  \nWith all my love and gratitude, This work is dedicated to you.=  \nGhazal  \nAbstract  \nAccurate sales forecasting in supply chain management remains is a central challenge that directly impacts inventory control, logistics efficiency, and strategic planning. In order to assess how well classic statistical, hybrid, and machine learning models predict actual, seasonal sales data from a large retail dataset, this thesis compares them using features like lagged sales, price, discount, and time-based indicators in a panel of ninety-six products observed over thirty three months.  \nFour forecasting models were considered: Pooled Ordinary Least Squares (OLS), Prophet (a modular trend-seasonality-holiday model), Random Forest, and XGBoost. All models were trained and evaluated under a unified preprocessing and validation framework, using standardized metrics including RMSE, MAE, MAPE, and R². Random Forest consistently outperformed all other models, achieving the highest accuracy and lowest error rates, followed by XGBoost. Prophet demonstrated competitive performance in capturing seasonality but underperformed during promotion-driven volatility. Pooled OLS, while the least a","cbCaiakWylSF5XNp","https://ap.wps.com/l/cbCaiakWylSF5XNp","pdf",1746218,1,98,"English","en",105,"# Chapter 1 Introduction\n## Background\n## Problem Statement\n## Research Objectives\n## Research Questions\n## Scope of the Study\n## Thesis Structure\n# Chapter 2 Literature Review","[{\"question\":\"Which forecasting models are compared in the thesis?\",\"answer\":\"The thesis compares Pooled Ordinary Least Squares (OLS), Prophet, Random Forest, and XGBoost under a unified preprocessing and validation framework.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Models are evaluated using standardized metrics including RMSE, MAE, MAPE, and R², after training on structured features such as lagged sales, price, discounts, and time indicators.\"},{\"question\":\"What is the main finding about model accuracy?\",\"answer\":\"Random Forest consistently outperforms the other models with the highest accuracy and lowest error rates, followed by XGBoost, while Prophet performs well on seasonality but underperforms during promotion-driven volatility.\"}]","Comparative Analysis of Machine Learning Models for Time Series Sales Forecasting in Supply Chain Management - Master Thesis | PDF",1785898948,247,{"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},"comparative-analysis-of-machine-learning-models-for-time-series-sales-forecasting-in-supply-chain-management-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/comparative-analysis-of-machine-learning-models-for-time-series-sales-forecasting-in-supply-chain-management-master-thesis/125440/",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","2026-08-05",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 forecasting models are compared in the thesis?","Question",{"text":75,"@type":76},"The thesis compares Pooled Ordinary Least Squares (OLS), Prophet, Random Forest, and XGBoost under a unified preprocessing and validation framework.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is model performance evaluated?",{"text":80,"@type":76},"Models are evaluated using standardized metrics including RMSE, MAE, MAPE, and R², after training on structured features such as lagged sales, price, discounts, and time indicators.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main finding about model accuracy?",{"text":84,"@type":76},"Random Forest consistently outperforms the other models with the highest accuracy and lowest error rates, followed by XGBoost, while Prophet performs well on seasonality but underperforms during promotion-driven volatility.","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"]