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
Diserahkan: 5 Mei 2023/Diterima: 23 Oktober 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
RUJUKAN
Aryal, S., Nadarajah,
D., Kasthurirathna, D., Rupasinghe,
L. & Jayawardena, C. 2019. Comparative analysis of the application of Deep
Learning techniques for Forex Rate prediction. International Conference on
Advancements in Computing (ICAC). pp. 329-333. https://doi.org/10.1109/ICAC49085.2019.9103428
Bai, S., Kolter, J.Z. & Koltun, V.
2018. An empirical evaluation of generic convolutional and recurrent networks
for sequence modeling. ArXiv Preprint ArXiv: 1803.01271.
https://doi.org/10.48550/arXiv.1803.01271
Cheng,
W., Wang, Y., Peng, Z., Ren, X., Shuai, Y., Zang, S., Liu, H., Cheng, H. & Wu, J. 2021.
High-efficiency chaotic time series prediction based on time convolution neural
network. Chaos, Solitons & Fractals 152: 111304.
https://doi.org/10.1016/j.chaos.2021.111304
Cao, J.,
Li, Z. & Li, J. 2019. Financial time series forecasting model based on
CEEMDAN and LSTM. Physica A:
Statistical Mechanics and its Applications 519: 127-139.
https://doi.org/10.1016/j.physa.2018.11.061
Das,
S.R., Mishra, D. & Rout, M. 2019. A hybridized ELM using self-adaptive
multi-population-based Jaya algorithm for currency exchange prediction: An
empirical assessment. Neural Computing and Applications 31(11):
7071-7094. https://doi.org/10.1007/s00521-018-3552-8
Deng, T.,
He, X. & Zeng, Z. 2018. Recurrent neural network for combined economic and
emission dispatch. Applied Intelligence 48: 2180-2198.
https://doi.org/10.1007/s10489-017-1072-3
Diebold,
F.X. & Mariano, R.S. 2002.
Comparing predictive accuracy. Journal of Business & Economic Statistics 20(1): 134-144. https://doi.org/10.1198/073500102753410444
Dragomiretskiy, K. & Zosso,
D. 2013. Variational mode decomposition. IEEE
Transactions on Signal Processing 62(3): 531-544.
https://doi.org10.1109/TSP.2013.2288675
Fan, J.,
Zhang, K., Zhu, Y. & Chen, B. 2021. Parallel spatio-temporal
attention-based TCN for multivariate time series prediction. Neural
Computing and Applications 35: 13109-13118. https://doi.org/10.1007/s00521-021-05958-z
Guo, H., Zhang, D., Liu, S., Wang, L.
& Ding, Y. 2021. Bitcoin price forecasting: A perspective of underlying blockchain transactions. Decision Support Systems 151: 113650. https://doi.org/10.1016/j.dss.2021.113650
Hu,
Z., Zhao, Y. & Khushi,
M. 2021. A survey of Forex and stock price prediction using deep
learning. Applied System Innovation 4(1): 9. https://doi.org/10.3390/asi4010009
Hua, Y.
& Zehao, C. 2020. Short-term traffic flow
prediction based on temporal convolutional networks. Journal of South China
University of Technology (Natural Science Edition) 48: 8. https://doi.org/10.1109/ITSC48978.2021.9564803
Jun, W., Lingyu, T., Yuyan, L. & Peng,
G. 2017. A weighted EMD-based prediction model based on TOPSIS and feed forward
neural network for noised timeseries. Knowledge-Based
Systems 132: 167-178. https://doi.org/10.1016/j.knosys.2017.06.022
Karevan, Z. & Suykens,
J.A.K. 2020. Transductive LSTM for time-series
prediction: An application to weather forecasting. Neural Networks 125:
1-9. https://doi.org/10.1016/j.neunet.2019.12.030
Long, J., Shelhamer, E. & Darrell, T. 2015. Fully
convolutional networks for semantic segmentation. Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition. pp. 3431-3440.
https://doi.org/10.1109/CVPR.2015.7298965
Loureiro, A.L., Miguéis,
V.L. & da Silva, L.F. 2018. Exploring the use of deep neural networks for
sales forecasting in fashion retail. Decision Support Systems 114:
81-93. https://doi.org/10.1016/j.dss.2018.08.010
Peng, Z.,
Peng, S., Fu, L., Lu, B., Tang, J., Wang, K. & Li, W. 2020. A novel deep
learning ensemble model with data denoising for
short-term wind speed forecasting. Energy Conversion and Management 207:
112524. https://doi.org/10.1016/j.enconman.2019.112524
Torres,
M.E., Colominas, M.A., Schlotthauer,
G. & Flandrin, P. 2011. A complete ensemble
empirical mode decomposition with adaptive noise. IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP). pp.
4144-4147. https://doi.org/10.1109/ICASSP.2011.5947265
Ulina, M., Purba,
R. & Halim, A. 2020. Foreign exchange prediction using CEEMDAN and improved
FA-LSTM. Fifth International Conference on Informatics and Computing (ICIC).
pp. 1-6. https://doi.org/10.1109/ICIC50835.2020.9288615
Wei, Y.,
Sun, S., Ma, J., Wang, S. & Lai, K.K. 2019. A decomposition clustering
ensemble learning approach for forecasting foreign exchange rates. Journal
of Management Science and Engineering 4(1): 45-54. https://doi.org/10.1016/j.jmse.2019.02.001
Yang, W.,
Wang, J., Niu, T. & Du, P. 2019. A hybrid
forecasting system based on a dual decomposition strategy and multi-objective
optimization for electricity price forecasting. Applied Energy 235:
1205-1225. https://doi.org/10.1016/j.apenergy.2018.11.034
Yasir, M., Durrani,
M.Y., Afzal, S., Maqsood, M., Aadil,
F., Mehmood, I. & Rho, S. 2019. An intelligent
event-sentiment-based daily foreign exchange rate forecasting system. Applied
Sciences 9(15): 2980. https://doi.org/10.3390/app9152980
Yildirim, D.C., Toroslu, I.H. & Fiore, U. 2021. Forecasting directional
movement of Forex data using LSTM with technical and macroeconomic indicators. Financial
Innovation 7(1): 1-36. https://doi.org/10.1186/s40854-020-00220-2
Yujia, Z., Zhicheng,
D., Songwei, G. & Jirong,
W. 2020. Dynamic personalized search based on RNN with attention mechanism. Chinese
Journal of Computers 43: 812-826. https://doi.org/10.11897/SP.J.1016.2020.00812
Zhang,
C., Pan, H., Ma, Y. & Huang, X. 2019. Analysis of Asia Pacific stock
markets with a novel multiscale model. Physica A: Statistical Mechanics and its Applications 534: 120939.
https://doi.org/10.1016/j.physa.2019.04.175
Zhang,
T., Tang, Z., Wu, J., Du, X. & Chen, K. 2021. Multi-step-ahead crude oil
price forecasting based on two-layer decomposition technique and extreme
learning machine optimized by the particle swarm optimization algorithm. Energy 229:
120797. https://doi.org/10.1016/j.energy.2021.120797
Zhen, Y., Fang, J., Zhao, X., Ge, J.
& Xiao, Y. 2022. Temporal convolution network based on attention mechanism
for well production prediction. Journal of Petroleum Science and
Engineering 218: 111043.
https://doi.org/10.1016/j.petrol.2022.111043
Zhou, J. & Wang, S. 2021. A carbon price prediction model
based on the secondary decomposition algorithm and influencing factors. Energies 14(5):
1328. https://doi.org/10.3390/en14051328
*Pengarang untuk surat-menyurat; email: Farhat.iqbal@um.uob.edu.pk
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