Univariate time series forecasting : comparing ARIMA & LSTM neural network to the random walk benchmark for exchange rates
Autor(a) principal: | |
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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/10400.5/24726 |
Resumo: | Mestrado Bolonha em Data Analytics for Business |
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7160 |
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Univariate time series forecasting : comparing ARIMA & LSTM neural network to the random walk benchmark for exchange ratesExchange rate forecastingunivariate time seriesARIMALSTMRNNDriftless Random WalkMestrado Bolonha em Data Analytics for BusinessThe difficulty of forecasting Exchange Rates has been a longstanding problem for economists and data analysts around the world. Nevertheless, a model that could produce accurate forecasts and outperform the random walk (RW) benchmark would be beneficial to policymakers and investors as it might help mitigate the effects of inflation, thus, having a real impact on the economic perspective. The objective of this paper is to develop and analyze the results of an ARIMA and LSTM models and determine if univariate time series models can show an improved accuracy at a 5-day and 60- day time horizon compared to the driftless random walk (DRW) model, which is the proposed benchmark in this study. In order to perform this analysis, daily exchange rate data for the currency pair USD/EUR was retrieved from the United States Board of Governors of the Federal Reserve System download data program, and later cleaned and manipulated using Python to produce forecasts for each of the models. The predictive accuracy was calculated, and the errors were measured with the MAPE, RMSE, and MSE metrics. Among the three models tested, I concluded that both the LSTM and ARIMA models outperformed the DRW benchmark at the 60-day horizon, which adds evidence of the suitability of autoregressive and machine learning univariate models when forecasting exchange rates, and of their potentially superior performance compared to classical models based on economic fundamentals, which have traditionally failed to outperform the RW benchmark. On the contrary, in the 5-day horizon, the DRW model showed the highest predictive accuracy, thus supporting the existing literature in the difficulty of forecasting exchange rates at short horizons.Instituto Superior de Economia e GestãoSobreira, NunoRepositório da Universidade de LisboaSánchez Gavilanes, Ricardo Andrés2022-12-30T01:30:24Z2022-032022-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.5/24726engSánchez Gavilanes , Ricardo Andrés (2022). “Univariate time series forecasting : comparing ARIMA & LSTM neural network to the random walk benchmark for exchange rates”. Dissertação de Mestrado. Universidade de Lisboa. Instituto Superior de Economia e Gestãoinfo: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:RCAAP2023-03-06T14:54:20Zoai:www.repository.utl.pt:10400.5/24726Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:08:42.411064Repositório 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 |
Univariate time series forecasting : comparing ARIMA & LSTM neural network to the random walk benchmark for exchange rates |
title |
Univariate time series forecasting : comparing ARIMA & LSTM neural network to the random walk benchmark for exchange rates |
spellingShingle |
Univariate time series forecasting : comparing ARIMA & LSTM neural network to the random walk benchmark for exchange rates Sánchez Gavilanes, Ricardo Andrés Exchange rate forecasting univariate time series ARIMA LSTM RNN Driftless Random Walk |
title_short |
Univariate time series forecasting : comparing ARIMA & LSTM neural network to the random walk benchmark for exchange rates |
title_full |
Univariate time series forecasting : comparing ARIMA & LSTM neural network to the random walk benchmark for exchange rates |
title_fullStr |
Univariate time series forecasting : comparing ARIMA & LSTM neural network to the random walk benchmark for exchange rates |
title_full_unstemmed |
Univariate time series forecasting : comparing ARIMA & LSTM neural network to the random walk benchmark for exchange rates |
title_sort |
Univariate time series forecasting : comparing ARIMA & LSTM neural network to the random walk benchmark for exchange rates |
author |
Sánchez Gavilanes, Ricardo Andrés |
author_facet |
Sánchez Gavilanes, Ricardo Andrés |
author_role |
author |
dc.contributor.none.fl_str_mv |
Sobreira, Nuno Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Sánchez Gavilanes, Ricardo Andrés |
dc.subject.por.fl_str_mv |
Exchange rate forecasting univariate time series ARIMA LSTM RNN Driftless Random Walk |
topic |
Exchange rate forecasting univariate time series ARIMA LSTM RNN Driftless Random Walk |
description |
Mestrado Bolonha em Data Analytics for Business |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12-30T01:30:24Z 2022-03 2022-03-01T00:00:00Z |
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/10400.5/24726 |
url |
http://hdl.handle.net/10400.5/24726 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Sánchez Gavilanes , Ricardo Andrés (2022). “Univariate time series forecasting : comparing ARIMA & LSTM neural network to the random walk benchmark for exchange rates”. Dissertação de Mestrado. Universidade de Lisboa. Instituto Superior de Economia e Gestão |
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.publisher.none.fl_str_mv |
Instituto Superior de Economia e Gestão |
publisher.none.fl_str_mv |
Instituto Superior de Economia e Gestão |
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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1799131181405437952 |