Time series forecasting for a call center in a Warsaw holding company

Detalhes bibliográficos
Autor(a) principal: Leszko, Dominika
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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spelling 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
repository.mail.fl_str_mv
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