Forecasting the Future at Siemens: Innovations in Time Series Analysis with Machine Learning Models

Bibliographic Details
Main Author: Simões, Guilherme Costa Marques Lopes
Publication Date: 2024
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/165293
Summary: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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spelling Forecasting the Future at Siemens: Innovations in Time Series Analysis with Machine Learning ModelsForecastingMachine Learning ModelsPythonTime SeriesSiemensBusiness AnalyticsRevenue ForecastingDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThis 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.Pinheiro, Flávio Luís PortasRUNSimões, Guilherme Costa Marques Lopes2024-03-22T13:36:34Z2024-02-082024-02-08T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/165293TID:203553640enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:19:51Zoai:run.unl.pt:10362/165293Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:50:38.689679Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Forecasting the Future at Siemens: Innovations in Time Series Analysis with Machine Learning Models
title Forecasting the Future at Siemens: Innovations in Time Series Analysis with Machine Learning Models
spellingShingle Forecasting the Future at Siemens: Innovations in Time Series Analysis with Machine Learning Models
Simões, Guilherme Costa Marques Lopes
Forecasting
Machine Learning Models
Python
Time Series
Siemens
Business Analytics
Revenue Forecasting
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Forecasting the Future at Siemens: Innovations in Time Series Analysis with Machine Learning Models
title_full Forecasting the Future at Siemens: Innovations in Time Series Analysis with Machine Learning Models
title_fullStr Forecasting the Future at Siemens: Innovations in Time Series Analysis with Machine Learning Models
title_full_unstemmed Forecasting the Future at Siemens: Innovations in Time Series Analysis with Machine Learning Models
title_sort Forecasting the Future at Siemens: Innovations in Time Series Analysis with Machine Learning Models
author Simões, Guilherme Costa Marques Lopes
author_facet Simões, Guilherme Costa Marques Lopes
author_role author
dc.contributor.none.fl_str_mv Pinheiro, Flávio Luís Portas
RUN
dc.contributor.author.fl_str_mv Simões, Guilherme Costa Marques Lopes
dc.subject.por.fl_str_mv Forecasting
Machine Learning Models
Python
Time Series
Siemens
Business Analytics
Revenue Forecasting
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Forecasting
Machine Learning Models
Python
Time Series
Siemens
Business Analytics
Revenue Forecasting
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2024
dc.date.none.fl_str_mv 2024-03-22T13:36:34Z
2024-02-08
2024-02-08T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/165293
TID:203553640
url http://hdl.handle.net/10362/165293
identifier_str_mv TID:203553640
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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