A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction
Abstract
:1. Introduction
2. Methodological Framework
2.1. Variational Mode Decomposition
- The Hilbert transform of each mode is calculated and then transformed into a respective uni-sided frequency spectrum;
- To estimate the center frequency of each mode , the mode is multiplied by the exponential tuned signal. This will modulate the mode spectra to the relevant baseband;
- The bandwidth of each mode is obtained by conducting the Gaussian smoothness on the demodulated signal.
2.2. Bidirectional Gated Recurrent Unit Model
2.3. Data
2.4. Data Augmentation
2.5. The Proposed Model
2.6. Evaluation Measures
2.7. Benchmark Models and Parameter Values
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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EUR/SAR Exchange Rate | EUR/CNY Exchange Rate | |
---|---|---|
Mean | 4.480 | 8.465 |
Std. Dev. | 0.597 | 1.167 |
Minimum | 3.173 | 6.652 |
Maximum | 5.916 | 11.222 |
Skewness | −0.058 | 0.642 |
Kurtosis | 2.624 | 2.155 |
Jarque–Bera | 1.859 | 28.348 *** |
Models | Training | Testing | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
MSE | MAE | MAPE | DA (%) | MSE | MAE | MAPE | DA (%) | |||
Ridge | 0.0179 | 0.0993 | 0.0218 | 0.9551 | 51.77 | 0.0072 | 0.0704 | 0.0170 | 0.8377 | 45.61 |
MLP | 0.0436 | 0.1634 | 0.0366 | 0.8907 | 56.63 | 0.0295 | 0.1533 | 0.0371 | 0.3319 | 43.86 |
LightGBM | 0.0089 | 0.0633 | 0.0139 | 0.9777 | 79.65 | 0.0112 | 0.0860 | 0.0210 | 0.7464 | 57.90 |
ANN | 0.0174 | 0.0981 | 0.0214 | 0.9565 | 53.98 | 0.0074 | 0.0720 | 0.0173 | 0.8333 | 45.61 |
V-ANN | 0.0037 | 0.0476 | 0.0105 | 0.9907 | 81.86 | 0.0025 | 0.0396 | 0.0097 | 0.9442 | 82.46 |
MV-ANN | 0.0194 | 0.1078 | 0.0233 | 0.9514 | 54.87 | 0.0075 | 0.0663 | 0.0159 | 0.8312 | 63.16 |
RNN | 0.0179 | 0.1009 | 0.0224 | 0.9552 | 57.52 | 0.0115 | 0.0861 | 0.0209 | 0.7401 | 49.12 |
V-RNN | 0.0036 | 0.0481 | 0.0108 | 0.9909 | 81.86 | 0.0018 | 0.0341 | 0.0082 | 0.9597 | 89.47 |
MV-RNN | 0.0345 | 0.1429 | 0.0318 | 0.9136 | 61.50 | 0.0484 | 0.1866 | 0.0458 | 0.8368 | 57.37 |
BiLSTM | 0.0176 | 0.0987 | 0.0216 | 0.9559 | 54.42 | 0.0075 | 0.0727 | 0.0175 | 0.8302 | 45.61 |
V-BiLSTM | 0.0036 | 0.0481 | 0.0108 | 0.9909 | 82.31 | 0.0020 | 0.0350 | 0.0085 | 0.9554 | 85.97 |
MVO-BiLSTM | 0.0025 | 0.0399 | 0.0088 | 0.9938 | 82.32 | 0.0011 | 0.0262 | 0.0063 | 0.9744 | 89.49 |
BiGRU | 0.0175 | 0.0986 | 0.0216 | 0.9561 | 53.54 | 0.0077 | 0.0737 | 0.0178 | 0.8264 | 45.61 |
V-BiGRU | 0.0041 | 0.0516 | 0.0118 | 0.9897 | 81.86 | 0.0029 | 0.0446 | 0.0109 | 0.9336 | 82.46 |
MVO-BiGRU | 0.0015 | 0.0303 | 0.0066 | 0.9963 | 89.38 | 0.0007 | 0.0208 | 0.0050 | 0.9846 | 94.77 |
Models | Training | Testing | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
MSE | MAE | MAPE | DA (%) | MSE | MAE | MAPE | DA (%) | |||
Ridge | 0.0610 | 0.1830 | 0.0211 | 0.9578 | 53.09 | 0.0153 | 0.0988 | 0.0130 | 0.8524 | 54.39 |
MLP | 0.1060 | 0.2465 | 0.0286 | 0.9267 | 54.42 | 0.0345 | 0.1570 | 0.0209 | 0.6683 | 56.14 |
LightGBM | 0.0200 | 0.1015 | 0.0117 | 0.9862 | 80.09 | 0.0301 | 0.1372 | 0.0179 | 0.7105 | 50.88 |
ANN | 0.0583 | 0.1791 | 0.0207 | 0.9597 | 54.87 | 0.0157 | 0.0995 | 0.0131 | 0.8486 | 50.88 |
V-ANN | 0.0134 | 0.0946 | 0.0109 | 0.9907 | 80.53 | 0.0111 | 0.0832 | 0.0108 | 0.8930 | 75.43 |
MV-ANN | 0.0693 | 0.1971 | 0.0229 | 0.952 | 63.72 | 0.0190 | 0.1101 | 0.0146 | 0.8177 | 56.14 |
RNN | 0.0605 | 0.1881 | 0.0218 | 0.9582 | 53.54 | 0.0230 | 0.1192 | 0.0155 | 0.7790 | 52.63 |
V-RNN | 0.0129 | 0.0925 | 0.0107 | 0.9911 | 80.53 | 0.0626 | 0.1860 | 0.0254 | 0.3979 | 70.18 |
MV-RNN | 0.1163 | 0.2571 | 0.0296 | 0.9196 | 55.75 | 0.0325 | 0.1467 | 0.0193 | 0.6871 | 56.14 |
BiLSTM | 0.0562 | 0.177 | 0.0204 | 0.9611 | 56.64 | 0.0163 | 0.1038 | 0.0136 | 0.8435 | 45.61 |
V-BiLSTM | 0.0129 | 0.0924 | 0.0106 | 0.9911 | 80.53 | 0.0109 | 0.0810 | 0.0106 | 0.8955 | 75.44 |
MVO-BiLSTM | 0.0049 | 0.0528 | 0.0061 | 0.9966 | 88.05 | 0.0063 | 0.0587 | 0.0076 | 0.9392 | 78.95 |
BiGRU | 0.0560 | 0.1773 | 0.0205 | 0.9613 | 53.98 | 0.0166 | 0.1056 | 0.0139 | 0.8405 | 45.61 |
V-BiGRU | 0.0128 | 0.0918 | 0.0106 | 0.9911 | 80.97 | 0.0147 | 0.0959 | 0.0124 | 0.8586 | 70.18 |
MVO-BiGRU | 0.0010 | 0.0249 | 0.0029 | 0.9993 | 91.15 | 0.0039 | 0.051 | 0.0067 | 0.9624 | 85.96 |
Models | EUR/SAR | EUR/CNY | ||||
---|---|---|---|---|---|---|
MSE | MAE | MAPE | MSE | MAE | MAPE | |
Ridge | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
MLP | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
LightGBM | 0.002 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
ANN | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
V-ANN | 0.006 | 0.000 | 0.000 | 0.003 | 0.000 | 0.000 |
MV-ANN | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
RNN | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
V-RNN | 0.000 | 0.000 | 0.000 | 0.075 | 0.046 | 0.050 |
MV-RNN | 0.005 | 0.000 | 0.000 | 0.001 | 0.000 | 0.000 |
BiLSTM | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
V-BiLSTM | 0.002 | 0.000 | 0.000 | 0.003 | 0.000 | 0.000 |
MVO-BiLSTM | 0.046 | 0.036 | 0.043 | 0.327 | 0.517 | 0.546 |
BiGRU | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
V-BiGRU | 0.002 | 0.000 | 0.000 | 0.003 | 0.000 | 0.000 |
MVO-BiGRU | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
Models | EUR/SAR | EUR/CNY | ||
---|---|---|---|---|
DA (%) | p-Value | DA (%) | p-Value | |
Ridge | 45.61 | 0.316 | 54.39 | 0.869 |
MLP | 43.86 | 0.316 | 56.14 | 0.963 |
LightGBM | 57.90 | 0.513 | 50.88 | 0.977 |
ANN | 45.61 | 0.479 | 50.88 | 0.796 |
V-ANN | 82.46 *** | 0.000 | 75.43 *** | 0.000 |
MV-ANN | 63.16 | 0.233 | 56.14 | 0.963 |
RNN | 49.12 | 0.423 | 52.63 | 0.844 |
V-RNN | 89.47 *** | 0.000 | 70.18 *** | 0.000 |
MV-RNN | 57.37 | 0.034 | 56.14 | 0.156 |
BiLSTM | 45.61 | 0.316 | 45.61 | 0.703 |
V-BiLSTM | 85.97 *** | 0.000 | 75.44 *** | 0.000 |
MVO-BiLSTM | 89.49 *** | 0.000 | 78.95 *** | 0.000 |
BiGRU | 45.61 | 0.409 | 45.61 | 0.703 |
V-BiGRU | 82.46 *** | 0.000 | 70.18 *** | 0.000 |
MVO-BiGRU | 94.77 *** | 0.000 | 85.96 *** | 0.000 |
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Iqbal, F.; Koutmos, D.; Ahmed, E.A.; Al-Essa, L.M. A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction. Risks 2024, 12, 139. https://doi.org/10.3390/risks12090139
Iqbal F, Koutmos D, Ahmed EA, Al-Essa LM. A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction. Risks. 2024; 12(9):139. https://doi.org/10.3390/risks12090139
Chicago/Turabian StyleIqbal, Farhat, Dimitrios Koutmos, Eman A. Ahmed, and Lulwah M. Al-Essa. 2024. "A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction" Risks 12, no. 9: 139. https://doi.org/10.3390/risks12090139