Time series forecasting for a call center in a Warsaw holding company
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/102939 |
Resumo: | Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Time series forecasting for a call center in a Warsaw holding companyMachine learningTime Series AnalysisARIMAForecastingSupervised LearningPredictive ModelsArtificial Neural NetworksRecurrent Neural NetworksGated Recurrent UnitLong Short Term MemoryInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn recent years, artificial intelligence and cognitive technologies are actively being adopted in industries that use conversational marketing. Workforce managers face the constant challenge of balancing the priorities of service levels and related service costs. This problem is especially common when inaccurate forecasts lead to inefficient scheduling decisions and in turn result in dramatic impact on the customer engagement and experience and thus call center’s profitability. The main trigger of this project development was the Company X’s struggle to estimate the number of inbound phone calls expected in the upcoming 40 days. Accurate phone call volume forecast could significantly improve consultants’ time management, as well as, service quality. Keeping this goal in mind, the main focus of this internship is to conduct a set of experiments with various types of predictive models and identify the best performing for the analyzed use case. After a thorough review of literature covering work related to time series analysis, the empirical part of the internship follows which describes the process of developing both, univariate and multivariate statistical models. The methods used in the report also include two types of recurrent neural networks which are commonly used for time series prediction. The exogenous variables used in multivariate models are derived from the Media Planning department of the company which stores information about the ads being published in the newspapers. The outcome of the research shows that statistical models outperformed the neural networks in this specific application. This report covers the overview of statistical and neural network models used. After that, a comparative study of all tested models is conducted and one best performing model is selected. Evidently, the experiments showed that SARIMAX model yields best predictions for the analyzed use-case and thus it is recommended for the company to be used for a better staff management driving a more pleasant customer experience of the call center.Pinheiro, Flávio Luís PortasBrach, PawelRUNLeszko, Dominika2020-08-27T15:32:12Z2020-07-062020-07-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/102939TID:202511758enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:48:30Zoai:run.unl.pt:10362/102939Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:39:47.138128Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Time series forecasting for a call center in a Warsaw holding company |
title |
Time series forecasting for a call center in a Warsaw holding company |
spellingShingle |
Time series forecasting for a call center in a Warsaw holding company Leszko, Dominika Machine learning Time Series Analysis ARIMA Forecasting Supervised Learning Predictive Models Artificial Neural Networks Recurrent Neural Networks Gated Recurrent Unit Long Short Term Memory |
title_short |
Time series forecasting for a call center in a Warsaw holding company |
title_full |
Time series forecasting for a call center in a Warsaw holding company |
title_fullStr |
Time series forecasting for a call center in a Warsaw holding company |
title_full_unstemmed |
Time series forecasting for a call center in a Warsaw holding company |
title_sort |
Time series forecasting for a call center in a Warsaw holding company |
author |
Leszko, Dominika |
author_facet |
Leszko, Dominika |
author_role |
author |
dc.contributor.none.fl_str_mv |
Pinheiro, Flávio Luís Portas Brach, Pawel RUN |
dc.contributor.author.fl_str_mv |
Leszko, Dominika |
dc.subject.por.fl_str_mv |
Machine learning Time Series Analysis ARIMA Forecasting Supervised Learning Predictive Models Artificial Neural Networks Recurrent Neural Networks Gated Recurrent Unit Long Short Term Memory |
topic |
Machine learning Time Series Analysis ARIMA Forecasting Supervised Learning Predictive Models Artificial Neural Networks Recurrent Neural Networks Gated Recurrent Unit Long Short Term Memory |
description |
Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-08-27T15:32:12Z 2020-07-06 2020-07-06T00:00:00Z |
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/10362/102939 TID:202511758 |
url |
http://hdl.handle.net/10362/102939 |
identifier_str_mv |
TID:202511758 |
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:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138014394318849 |