[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120054-en":3,"doc-seo-120054-105":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},120054,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Forecasting the Future at Siemens - Innovations in Time Series Analysis with Machine Learning Models","This internship report presents an innovative investigation into corporate revenue forecasting using Python and advanced machine learning algorithms to outperform conventional approaches. A novel methodology combines different model selections and optimized parameters to increase predictive precision. The implementations yield a substantial improvement in prediction accuracy, producing a reliable revenue forecasting capability for Siemens. Despite limitations from the amount and diversity of input data, such as restricted historical records, the study demonstrates meaningful gains in time series prediction beyond traditional human methods.","Master Degree Program in  \nData Science and Advanced Analytics  \nMDSAA  \nForecasting the Future at Siemens: Innovations in Time Series Analysis with Machine Learning Models  \nGuilherme Costa Marques Lopes Simões  \nInternship Report  \npresented as partial requirement for obtaining the Master Degree Program 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 Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nFORECASTING THE FUTURE AT SIEMENS: INNOVATIONS IN TIME SERIES ANALYSIS WITH MACHINE LEARNING MODELS  \nby  \nGuilherme Costa Marques Lopes Simões  \nInternship report presented as partial requirement for obtaining the Master degree in Advanced Analytics, with a Specialization in Data Science.  \nSupervisor: prof Doutor Flávio Luís Portas Pinheiro  \nNovembre 2023  \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 acknowledge the Rules of Conduct and Code of Honor from the NOVA Information Management School.  \n22 November 2023  \nACKNOWLEDGEMENTS  \nA special acknowledgment goes to Mário Gonçalves, my recruiter and buddy at Siemens, who welcomed me warmly into the Siemens family. His readiness to assist in any matter needed was invaluable.  \nI am deeply grateful to João Cavaleiro, the team leader, for the trust he placed in me and for the extensive guidance, both professional and personal, he offered throughout these months. Working with you has been a pleasure, and I aspire to reach your level of expertise one day.  \nTo my entire team, thank you for making work dynamic and enjoyable. It will be challenging to find another team with the same spirit and commitment.  \nMy heartfelt gratitude also goes to my family, who made it possible for me to pursue this master's degree. Your encouragement to never give up and to stay focused has been a cornerstone of my journey.  \nFinally, I would like to thank all my friends who supported me in my decisions and helped me manage my concerns during this time. A special thanks to my friend and internship colleague, João Carvalho, for being an unwavering companion on this journey and for the enriching discussions we had about this project.  \nABSTRACT  \nThis internship report introduces an innovative research investigation in the field of revenue forecasting in corporate settings, utilising python, and sophisticated machine learning algorithms to outperform conventional forecasting approaches. This study employs a novel methodology by utilising different combinations of machine learning models and optimising parameters to improve the precision of predictive models. The implementations resulted in a considerable improvement in the prediction accuracy, making this application a reliable source of revenue prediction for Siemens. Despite the inherent constraints associated with the amount and variety of input data, like the limitation of historical data, this study displays a noteworthy enhancement in time series prediction, surpassing traditional human methods. This dissertation presents an original approach that offers a realistic demonstration of the application and efficacy of advanced machine learning techniques in the domain of revenue forecasting. The results provide significant insights and a solid basis for future advancements in the field of business analytics, hence facilitating the creation of more sophisticated and effective digital revenue forecasting systems.  \nKEYWORDS  \nForecasting; Machine Learning Models; Python; Time Series; Siemens; Business Analytics; Revenue Forecasting  \nINDEX  \n1. Introduction ..........................................................................","cbCaiugMqcheyDlc","https://ap.wps.com/l/cbCaiugMqcheyDlc","pdf",1755311,1,57,"English","en",105,"# Introduction\n# Theoretical Background\n## Time Series\n## Machine Learning Models\n# Methodology\n## Data\n## Algorithm\n## Forecast Granularity","[{\"question\":\"What problem does the internship report address?\",\"answer\":\"It addresses revenue forecasting in corporate settings, aiming to improve forecasting quality for Siemens.\"},{\"question\":\"Which tools and modeling approach does the report use?\",\"answer\":\"It uses Python with advanced machine learning algorithms, experimenting with different combinations of models and optimized parameters.\"},{\"question\":\"How does the report evaluate results despite data constraints?\",\"answer\":\"It reports considerable improvements in prediction accuracy for time series forecasting, while acknowledging constraints such as limited historical data and variety of inputs.\"}]","Forecasting the Future at Siemens - 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