Plotting Time: Exploring Visual Representations for Time Series Classification

Detalhes bibliográficos
Autor(a) principal: Marques, Brian Manuel Monteiro
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/58071
Resumo: Tese de mestrado, Engenharia Informática, 2022, Universidade de Lisboa, Faculdade de Ciências
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spelling Plotting Time: Exploring Visual Representations for Time Series ClassificationSeries TemporaisGeração de ImagensRedes Neuronais ConvolucionaisRepresentações GráficasTeses de mestrado - 2022Departamento de InformáticaTese de mestrado, Engenharia Informática, 2022, Universidade de Lisboa, Faculdade de CiênciasTime series data is a collection of data points acquired in successive order over a period of time, allowing us to obtain temporal information and make time-based predictions through the combination of Machine Learning (ML) algorithms. Time series are prevalent in crucial sectors for society’s development, such as Economy, Health, Weather, and Astronomy, with the objective of improving the quality of life through the prediction of climate changes, economic variations, earthquakes, and other types of events. These sectors require models with good predictive abilities and capable of scaling as the volume of data gradually increases. We can address this issue by using Deep Learning (DL) models that can keep a good performance while increasing the amount of data. One example is the Convolutional Neural Network (CNN), which uses images as input in several activity sectors. There is not much time series-related work with deep learning models and image generation. As a result, our objective is to develop new methods for image generation and then train them with a simple CNN. We focus on time series data to create a new algorithm for converting non-image time series data into graphical images that contain either box plots or violin plots with statistical information. We hypothesize that CNNs can interpret and learn different elements of the plots, and by comparing two different approaches, we can verify this statement. Our results indicate that CNNs may not understand some elements of the box and violin plots, for example, the outliers and quartiles, and focus more on the density and distribution of the data. In the future, it would be interesting to study alternative image generation algorithms and explore graphical representations in multivariate datasets.Silva, Sara Guilherme Oliveira daRepositório da Universidade de LisboaMarques, Brian Manuel Monteiro2023-06-07T14:20:39Z202220222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/58071enginfo: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:RCAAP2023-11-08T17:06:49Zoai:repositorio.ul.pt:10451/58071Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:08:27.235511Repositório 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 Plotting Time: Exploring Visual Representations for Time Series Classification
title Plotting Time: Exploring Visual Representations for Time Series Classification
spellingShingle Plotting Time: Exploring Visual Representations for Time Series Classification
Marques, Brian Manuel Monteiro
Series Temporais
Geração de Imagens
Redes Neuronais Convolucionais
Representações Gráficas
Teses de mestrado - 2022
Departamento de Informática
title_short Plotting Time: Exploring Visual Representations for Time Series Classification
title_full Plotting Time: Exploring Visual Representations for Time Series Classification
title_fullStr Plotting Time: Exploring Visual Representations for Time Series Classification
title_full_unstemmed Plotting Time: Exploring Visual Representations for Time Series Classification
title_sort Plotting Time: Exploring Visual Representations for Time Series Classification
author Marques, Brian Manuel Monteiro
author_facet Marques, Brian Manuel Monteiro
author_role author
dc.contributor.none.fl_str_mv Silva, Sara Guilherme Oliveira da
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Marques, Brian Manuel Monteiro
dc.subject.por.fl_str_mv Series Temporais
Geração de Imagens
Redes Neuronais Convolucionais
Representações Gráficas
Teses de mestrado - 2022
Departamento de Informática
topic Series Temporais
Geração de Imagens
Redes Neuronais Convolucionais
Representações Gráficas
Teses de mestrado - 2022
Departamento de Informática
description Tese de mestrado, Engenharia Informática, 2022, Universidade de Lisboa, Faculdade de Ciências
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022-01-01T00:00:00Z
2023-06-07T14:20:39Z
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/10451/58071
url http://hdl.handle.net/10451/58071
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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