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A hybrid approach for accurate forecasting of exchange rate prices using VMD-CEEMDAN-GRU-ATCN model

The foreign exchange (Forex) market has greatly influenced the global financial market. While Forex trading offers investors substantial yield prospects, some risks are also involved. It is challenging to accurately model financial time series due to their nonlinear, non-stationary and noisy propert...

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Main Authors: Kausar, Rehan, Iqbal, Farhat, Abdul Raziq,, Sheikh, Naveed
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2023
Online Access:http://journalarticle.ukm.my/23256/1/SB%2020.pdf
http://journalarticle.ukm.my/23256/
https://www.ukm.my/jsm/english_journals/vol52num11_2023/contentsVol52num11_2023.html
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spelling my-ukm.journal.232562024-03-22T01:11:38Z http://journalarticle.ukm.my/23256/ A hybrid approach for accurate forecasting of exchange rate prices using VMD-CEEMDAN-GRU-ATCN model Kausar, Rehan Iqbal, Farhat Abdul Raziq, Sheikh, Naveed The foreign exchange (Forex) market has greatly influenced the global financial market. While Forex trading offers investors substantial yield prospects, some risks are also involved. It is challenging to accurately model financial time series due to their nonlinear, non-stationary and noisy properties with an uncertain and hidden relationship. Thus, developing extremely precise forecasting techniques is crucial for investors and decision-makers. This study introduces a novel hybrid forecasting model, VMD-CEEMDAN-GRU-ATCN, designed to improve Forex price prediction accuracy. To begin with, our proposed model utilizes the variational model decomposition (VMD) technique for breaking down raw prices into multiple sub-components and residual terms. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique is utilized to extract features from the residual terms, which involves further decomposition and analysis of these complex information-containing terms. These sub-components are then predicted by the gated recurrent unit (GRU) model. To enhance the effectiveness of our hybrid model, we include the open, high, low, and close prices and seven Forex market technical indicators. Finally, an attention-based temporal convolutional network (ATCN) model is used to obtain the Forex price forecasts. For both one-step and multi-step ahead forecasting, our proposed VMD-CEEMDAN-GRU-ATCN model has demonstrated superior and consistent performance in predicting USD/PKR exchange rate price series. Penerbit Universiti Kebangsaan Malaysia 2023 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/23256/1/SB%2020.pdf Kausar, Rehan and Iqbal, Farhat and Abdul Raziq, and Sheikh, Naveed (2023) A hybrid approach for accurate forecasting of exchange rate prices using VMD-CEEMDAN-GRU-ATCN model. Sains Malaysiana, 52 (11). pp. 3293-3306. ISSN 0126-6039 https://www.ukm.my/jsm/english_journals/vol52num11_2023/contentsVol52num11_2023.html
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description The foreign exchange (Forex) market has greatly influenced the global financial market. While Forex trading offers investors substantial yield prospects, some risks are also involved. It is challenging to accurately model financial time series due to their nonlinear, non-stationary and noisy properties with an uncertain and hidden relationship. Thus, developing extremely precise forecasting techniques is crucial for investors and decision-makers. This study introduces a novel hybrid forecasting model, VMD-CEEMDAN-GRU-ATCN, designed to improve Forex price prediction accuracy. To begin with, our proposed model utilizes the variational model decomposition (VMD) technique for breaking down raw prices into multiple sub-components and residual terms. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique is utilized to extract features from the residual terms, which involves further decomposition and analysis of these complex information-containing terms. These sub-components are then predicted by the gated recurrent unit (GRU) model. To enhance the effectiveness of our hybrid model, we include the open, high, low, and close prices and seven Forex market technical indicators. Finally, an attention-based temporal convolutional network (ATCN) model is used to obtain the Forex price forecasts. For both one-step and multi-step ahead forecasting, our proposed VMD-CEEMDAN-GRU-ATCN model has demonstrated superior and consistent performance in predicting USD/PKR exchange rate price series.
format Article
author Kausar, Rehan
Iqbal, Farhat
Abdul Raziq,
Sheikh, Naveed
spellingShingle Kausar, Rehan
Iqbal, Farhat
Abdul Raziq,
Sheikh, Naveed
A hybrid approach for accurate forecasting of exchange rate prices using VMD-CEEMDAN-GRU-ATCN model
author_facet Kausar, Rehan
Iqbal, Farhat
Abdul Raziq,
Sheikh, Naveed
author_sort Kausar, Rehan
title A hybrid approach for accurate forecasting of exchange rate prices using VMD-CEEMDAN-GRU-ATCN model
title_short A hybrid approach for accurate forecasting of exchange rate prices using VMD-CEEMDAN-GRU-ATCN model
title_full A hybrid approach for accurate forecasting of exchange rate prices using VMD-CEEMDAN-GRU-ATCN model
title_fullStr A hybrid approach for accurate forecasting of exchange rate prices using VMD-CEEMDAN-GRU-ATCN model
title_full_unstemmed A hybrid approach for accurate forecasting of exchange rate prices using VMD-CEEMDAN-GRU-ATCN model
title_sort hybrid approach for accurate forecasting of exchange rate prices using vmd-ceemdan-gru-atcn model
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2023
url http://journalarticle.ukm.my/23256/1/SB%2020.pdf
http://journalarticle.ukm.my/23256/
https://www.ukm.my/jsm/english_journals/vol52num11_2023/contentsVol52num11_2023.html
_version_ 1794552832808976384
score 13.211869

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