Forecasting real exchange rate (REER) using artificial intelligence and time series models
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/37901 |
Resumo: | Forecasting is an attractive topic in every field of study because no one knows the exact nature of the underlying phenomena, but it can be guessed using mathematical functions. As the world progresses towards technology and betterment, algorithms are updated to understand the nature of ongoing phenomena. Machine learning (ML) algorithms are an updated phenomenon used in every task aspect. Real exchange rate data is assumed to be one of the significant components of the business market, which plays a pivotal role in learning market trends. In this work, machine learning models, i.e., the Multi-layer perceptron model (MLP), Extreme learning machine (ELM) model and classical time series models are used, Autoregressive integrated moving average (ARIMA) and Exponential Smoothing (ES) model to model and predict the real exchange rate data set (REER). The data under consideration is from January 2019 to June 2022 and comprises 864 observations. This study split the data set into training and testing and applied all stated models. This study selects a model that meets the Key Performance Indicators (KPI) criteria. This model was selected as the best candidate model to predict the behaviour of the real exchange rate data set. |
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Forecasting real exchange rate (REER) using artificial intelligence and time series modelsREERForecastingMachine learningMulti-layer perceptron modelExponential smoothingExtreme learning machineARIMAForecasting is an attractive topic in every field of study because no one knows the exact nature of the underlying phenomena, but it can be guessed using mathematical functions. As the world progresses towards technology and betterment, algorithms are updated to understand the nature of ongoing phenomena. Machine learning (ML) algorithms are an updated phenomenon used in every task aspect. Real exchange rate data is assumed to be one of the significant components of the business market, which plays a pivotal role in learning market trends. In this work, machine learning models, i.e., the Multi-layer perceptron model (MLP), Extreme learning machine (ELM) model and classical time series models are used, Autoregressive integrated moving average (ARIMA) and Exponential Smoothing (ES) model to model and predict the real exchange rate data set (REER). The data under consideration is from January 2019 to June 2022 and comprises 864 observations. This study split the data set into training and testing and applied all stated models. This study selects a model that meets the Key Performance Indicators (KPI) criteria. This model was selected as the best candidate model to predict the behaviour of the real exchange rate data set.Elsevier2023-05-26T13:39:39Z2023-05-05T00:00:00Z2023-05-05info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/37901eng10.1016/j.heliyon.2023.e16335Qureshi, MoizAhmad, NawazUllah, SaifRaza ul Mustafa, Ahmedinfo: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:RCAAP2024-02-22T12:14:00Zoai:ria.ua.pt:10773/37901Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:08:27.117231Repositório 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 |
Forecasting real exchange rate (REER) using artificial intelligence and time series models |
title |
Forecasting real exchange rate (REER) using artificial intelligence and time series models |
spellingShingle |
Forecasting real exchange rate (REER) using artificial intelligence and time series models Qureshi, Moiz REER Forecasting Machine learning Multi-layer perceptron model Exponential smoothing Extreme learning machine ARIMA |
title_short |
Forecasting real exchange rate (REER) using artificial intelligence and time series models |
title_full |
Forecasting real exchange rate (REER) using artificial intelligence and time series models |
title_fullStr |
Forecasting real exchange rate (REER) using artificial intelligence and time series models |
title_full_unstemmed |
Forecasting real exchange rate (REER) using artificial intelligence and time series models |
title_sort |
Forecasting real exchange rate (REER) using artificial intelligence and time series models |
author |
Qureshi, Moiz |
author_facet |
Qureshi, Moiz Ahmad, Nawaz Ullah, Saif Raza ul Mustafa, Ahmed |
author_role |
author |
author2 |
Ahmad, Nawaz Ullah, Saif Raza ul Mustafa, Ahmed |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Qureshi, Moiz Ahmad, Nawaz Ullah, Saif Raza ul Mustafa, Ahmed |
dc.subject.por.fl_str_mv |
REER Forecasting Machine learning Multi-layer perceptron model Exponential smoothing Extreme learning machine ARIMA |
topic |
REER Forecasting Machine learning Multi-layer perceptron model Exponential smoothing Extreme learning machine ARIMA |
description |
Forecasting is an attractive topic in every field of study because no one knows the exact nature of the underlying phenomena, but it can be guessed using mathematical functions. As the world progresses towards technology and betterment, algorithms are updated to understand the nature of ongoing phenomena. Machine learning (ML) algorithms are an updated phenomenon used in every task aspect. Real exchange rate data is assumed to be one of the significant components of the business market, which plays a pivotal role in learning market trends. In this work, machine learning models, i.e., the Multi-layer perceptron model (MLP), Extreme learning machine (ELM) model and classical time series models are used, Autoregressive integrated moving average (ARIMA) and Exponential Smoothing (ES) model to model and predict the real exchange rate data set (REER). The data under consideration is from January 2019 to June 2022 and comprises 864 observations. This study split the data set into training and testing and applied all stated models. This study selects a model that meets the Key Performance Indicators (KPI) criteria. This model was selected as the best candidate model to predict the behaviour of the real exchange rate data set. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-05-26T13:39:39Z 2023-05-05T00:00:00Z 2023-05-05 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10773/37901 |
url |
http://hdl.handle.net/10773/37901 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1016/j.heliyon.2023.e16335 |
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 |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
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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1799137737095249920 |