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Sains Malaysiana 52(11)(2023): 3293-3306
http://doi.org/10.17576/jsm-2023-5211-20
A Hybrid Approach for Accurate Forecasting of Exchange
Rate Prices using VMD-CEEMDAN-GRU-ATCN Model
(Pendekatan Hibrid untuk Ramalan Tepat Harga Kadar Pertukaran menggunakan Model
VMD-CEEMDAN-GRU-ATCN)
REHAN
KAUSAR1, FARHAT IQBAL2,3,*, ABDUL RAZIQ2 & NAVEED SHEIKH4
1Department of Statistics, Sardar Bahadur Khan Women’s University, Quetta, Pakistan
2Department of Statistics,
University of Balochistan, Quetta, Pakistan
3Department of Mathematics, Imam Abdulrahman Bin Faisal University, Saudi Arabia
4Department of Mathematics,
University of Balochistan, Quetta, Pakistan
Received: 5 May 2023/Accepted: 23 October 2023
Abstract
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.
Keywords:
Attention mechanism; Forex; dual decomposition strategy; hybrid deep learning
models; temporal convolutional network
Abstrak
Pasaran pertukaran asing (Forex) telah banyak mempengaruhi pasaran kewangan global. Walaupun perdagangan Forex menawarkan prospek hasil yang besar kepada pelabur, beberapa risiko turut terlibat. Adalah mencabar untuk memodelkan siri masa kewangan dengan tepat kerana sifatnya yang tidak linear, tidak pegun dan hingar dengan hubungan yang tidak pasti dan tersembunyi. Oleh itu, membangunkan teknik ramalan yang sangat tepat adalah penting untuk pelabur dan pembuat keputusan. Kajian ini memperkenalkan model ramalan hibrid baru, VMD-CEEMDAN-GRU-ATCN
yang direka untuk meningkatkan ketepatan ramalan harga Forex. Sebagai permulaan, model cadangan kami menggunakan teknik penguraian model variasi (VMD) untuk memecahkan harga mentah kepada terma berbilang sub-komponen dan sisa. Teknik penguraian mod empirik ensembel lengkap dengan hingar suai (CEEMDAN) digunakan untuk mengekstrak ciri daripada terma sisa yang melibatkan penguraian dan analisis lanjut bagi terma yang mengandungi maklumat yang kompleks ini. Sub-komponen ini kemudiannya diramalkan oleh model unit berulang berpagar (GRU). Untuk meningkatkan keberkesanan model hibrid ini, kami memasukkan harga terbuka, tinggi, rendah dan tertutup serta tujuh penunjuk teknikal pasaran Forex. Akhir sekali, model rangkaian konvolusi temporal berasaskan perhatian (ATCN) digunakan untuk mendapatkan ramalan harga Forex. Untuk ramalan selangkah dan berbilang langkah ke hadapan, model cadangan VMD-CEEMDAN-GRU-ATCN telah menunjukkan prestasi unggul dan tekal dalam meramalkan siri harga pertukaran USD/PKR.
Kata kunci: Forex; model pembelajaran mendalam hibrid; strategi penguraian dual; mekanisme perhatian; rangkaian konvolusi temporal
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*Corresponding
author; email: Farhat.iqbal@um.uob.edu.pk
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