Univariate time series forecasting : comparing ARIMA & LSTM neural network to the random walk benchmark for exchange rates

Bibliographic Details
Main Author: Sánchez Gavilanes, Ricardo Andrés
Publication Date: 2022
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10400.5/24726
Summary: Mestrado Bolonha em Data Analytics for Business
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network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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spelling 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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-17T15:43:10Zoai:repositorio.ulisboa.pt:10400.5/24726Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T03:51:40.033977Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833601860284448768

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