Time series forecasting on crime data in Amsterdam for a software company

Detalhes bibliográficos
Autor(a) principal: Singh, Prakash
Data de Publicação: 2018
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/57826
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 on crime data in Amsterdam for a software companyTime series analysisForecastingARIMASupervised learningMachine learningPredictive modelsNeural networksRecurrent Neural NetworksConvolutional neural networksRelatórios de EstágioInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn recent years, there have been many discussions of data mining technology implementation in the fight against terrorism and crime. Sentient as a software company has been supporting the police for years by applying data mining techniques in the DataDetective application (Sentient, 2017). Experimenting with various types of predictive model solutions, selecting the most efficient and promising solution are the objectives of this internship. Initially, extended literatures were reviewed in the field of data mining, crime analysis and crime data mining. Sentient provided 7 years of crime data which was aggregated on daily basis to create a univariate dataset. Also, an incidence type daily aggregation was done to create a multivariate dataset. The prediction length for each solution was 7 days. The experiments were divided into two major categories: Statistical models and neural network models. Neural networks outperformed statistical models for the crime data. This paper provides the overview of statistical models and neural network models used. A comparative study of all the models on similar dataset gives a clear picture of their performance on available data and generalization capability. Evidently, the experiments showed that Gated Recurrent units (GRU) produced better prediction in comparison to other models. In conclusion, gated recurrent unit implementation could give benefit to police in predicting crime. Hence, time series analysis using GRU could be a prospective additional feature in DataDetective.Mendes, Jorge MoraisHoekstra, VincentRUNSingh, Prakash2019-01-18T17:33:59Z2018-12-212018-12-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/57826TID:202150976enginfo: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:RCAAP2024-03-11T04:27:54Zoai:run.unl.pt:10362/57826Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:33:07.856302Repositório 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 on crime data in Amsterdam for a software company
title Time series forecasting on crime data in Amsterdam for a software company
spellingShingle Time series forecasting on crime data in Amsterdam for a software company
Singh, Prakash
Time series analysis
Forecasting
ARIMA
Supervised learning
Machine learning
Predictive models
Neural networks
Recurrent Neural Networks
Convolutional neural networks
Relatórios de Estágio
title_short Time series forecasting on crime data in Amsterdam for a software company
title_full Time series forecasting on crime data in Amsterdam for a software company
title_fullStr Time series forecasting on crime data in Amsterdam for a software company
title_full_unstemmed Time series forecasting on crime data in Amsterdam for a software company
title_sort Time series forecasting on crime data in Amsterdam for a software company
author Singh, Prakash
author_facet Singh, Prakash
author_role author
dc.contributor.none.fl_str_mv Mendes, Jorge Morais
Hoekstra, Vincent
RUN
dc.contributor.author.fl_str_mv Singh, Prakash
dc.subject.por.fl_str_mv Time series analysis
Forecasting
ARIMA
Supervised learning
Machine learning
Predictive models
Neural networks
Recurrent Neural Networks
Convolutional neural networks
Relatórios de Estágio
topic Time series analysis
Forecasting
ARIMA
Supervised learning
Machine learning
Predictive models
Neural networks
Recurrent Neural Networks
Convolutional neural networks
Relatórios de Estágio
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2018
dc.date.none.fl_str_mv 2018-12-21
2018-12-21T00:00:00Z
2019-01-18T17:33:59Z
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/10362/57826
TID:202150976
url http://hdl.handle.net/10362/57826
identifier_str_mv TID:202150976
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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