Predictive Modelling of the Bitcoin Price: A Comprehensive Analysis of Time Series Models - The usage of time-based models in predicting the price of Bitcoin in both the short and long term future

Detalhes bibliográficos
Autor(a) principal: Costa, Ana Rita Pires da
Data de Publicação: 2011
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/164639
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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spelling Predictive Modelling of the Bitcoin Price: A Comprehensive Analysis of Time Series Models - The usage of time-based models in predicting the price of Bitcoin in both the short and long term futureTime series forecastingPrice PredictionARIMAVARRNNLSTM LayersProphetSDG 8 - Decent work and economic growthSDG 9 - Industry, innovation and infrastructureDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoProject Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe extraordinary volatility of Bitcoin is attributed to a multitude of external factors that are difficult to identify and monitor for predictive modeling. The impact of the Bitcoin halving introduces still another level of complexity, implying possible stability in the far future, subject to discernible seasonality indicators. This thesis aims to identify what variables influence the value of bitcoin and what kind of models better forecast its price. Following the CRISP-DM methodology, data was collected from online sources, analyzed, and treated accordingly as means to be fed to different types of models: Autoregressive Integrated Moving Average (ARIMA), Vector Autoregressive (VAR), Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) Layers and Prophet. The variables that were found to have the greatest potential for inclusion in prediction models were Bitcoin volume, the VT index, and currencies like the Israeli New Shekel and Euro, as well as silver and copper. Although Prophet showed potential, it was clear that it had limitations, especially when it came to predicting long-term outcomes with variables other than Bitcoin. Furthermore, in these models, regressors appeared to be a promising technique, but could only be used to anticipate known futures. While the RNN model with LSTM layers seemed reliable for short-term forecasts, it was not practical for making large-scale, long-term investment decisions. These results highlight how difficult it is currently to develop predictive models that accurately predict Bitcoin values, particularly for large-scale, long-term decision-making in the investment market.Pinheiro, Flávio Luís PortasRUNCosta, Ana Rita Pires da2024-03-08T11:43:58Z2011-01-312011-01-31T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/164639TID:203543785enginfo: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:RCAAP2024-03-11T05:53:39Zoai:run.unl.pt:10362/164639Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T04:00:18.098353Repositório 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 Predictive Modelling of the Bitcoin Price: A Comprehensive Analysis of Time Series Models - The usage of time-based models in predicting the price of Bitcoin in both the short and long term future
title Predictive Modelling of the Bitcoin Price: A Comprehensive Analysis of Time Series Models - The usage of time-based models in predicting the price of Bitcoin in both the short and long term future
spellingShingle Predictive Modelling of the Bitcoin Price: A Comprehensive Analysis of Time Series Models - The usage of time-based models in predicting the price of Bitcoin in both the short and long term future
Costa, Ana Rita Pires da
Time series forecasting
Price Prediction
ARIMA
VAR
RNN
LSTM Layers
Prophet
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Predictive Modelling of the Bitcoin Price: A Comprehensive Analysis of Time Series Models - The usage of time-based models in predicting the price of Bitcoin in both the short and long term future
title_full Predictive Modelling of the Bitcoin Price: A Comprehensive Analysis of Time Series Models - The usage of time-based models in predicting the price of Bitcoin in both the short and long term future
title_fullStr Predictive Modelling of the Bitcoin Price: A Comprehensive Analysis of Time Series Models - The usage of time-based models in predicting the price of Bitcoin in both the short and long term future
title_full_unstemmed Predictive Modelling of the Bitcoin Price: A Comprehensive Analysis of Time Series Models - The usage of time-based models in predicting the price of Bitcoin in both the short and long term future
title_sort Predictive Modelling of the Bitcoin Price: A Comprehensive Analysis of Time Series Models - The usage of time-based models in predicting the price of Bitcoin in both the short and long term future
author Costa, Ana Rita Pires da
author_facet Costa, Ana Rita Pires da
author_role author
dc.contributor.none.fl_str_mv Pinheiro, Flávio Luís Portas
RUN
dc.contributor.author.fl_str_mv Costa, Ana Rita Pires da
dc.subject.por.fl_str_mv Time series forecasting
Price Prediction
ARIMA
VAR
RNN
LSTM Layers
Prophet
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Time series forecasting
Price Prediction
ARIMA
VAR
RNN
LSTM Layers
Prophet
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2011
dc.date.none.fl_str_mv 2011-01-31
2011-01-31T00:00:00Z
2024-03-08T11:43:58Z
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/10362/164639
TID:203543785
url http://hdl.handle.net/10362/164639
identifier_str_mv TID:203543785
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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