Hybrid Feature Reduction Using PCC-Stacked Autoencoders for Gold/Oil Prices Forecasting under COVID-19 Pandemic
The financial markets have been influenced by the emerging spread of Coronavirus disease, COVID-19. The oil, and gold as well have experienced a downward trend due to the increased rate in the number of confirmed COVID-19 cases. Lately, the published COVID data comprised new variables that may influ...
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| Vydáno v: | Electronics (Basel) Ročník 11; číslo 7; s. 991 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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01.04.2022
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| ISSN: | 2079-9292, 2079-9292 |
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| Abstract | The financial markets have been influenced by the emerging spread of Coronavirus disease, COVID-19. The oil, and gold as well have experienced a downward trend due to the increased rate in the number of confirmed COVID-19 cases. Lately, the published COVID data comprised new variables that may influence the accuracy of the oil/gold prices forecasting models including the Stringency index, Reproduction rate, Positive Rate, and Vaccinations. In this study, Deep Autoencoders are introduced and combined with the well-known approach: Pearson Correlation Coefficient, PCC, analysis in selecting the key features that affect the accuracy of the forecasting models of gold and oil prices with respect to COVID-19 pandemic. We have utilized a hybrid approach of PCC along with a 2-Stage Stacked Autoencoder, SA, to extract the latent features which are then submitted to Neural Network, NN, regression model. The NN regressor has been trained using the Bayesian Regularization-backpropagation algorithm which provides a good generalization for small noisy datasets. The hybrid approach has yielded the minimum MSE values of 8.97 × 10−3 and 5.356 × 10−2 on the oil/gold validation set, respectively. Compared to the existing approaches, the proposed approach has outperformed the ARIMA, ML based regression models in forecasting the oil/gold prices. In addition, the introduced framework has yielded lower Mean Absolute Error, MAE, than the Recurrent Neural Network, RNN, and the Principal Component Analysis, PCA, for dimension reduction. The retrieved results showed that the hybrid method produced more robust features by considering the relationship between the input features. |
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| AbstractList | The financial markets have been influenced by the emerging spread of Coronavirus disease, COVID-19. The oil, and gold as well have experienced a downward trend due to the increased rate in the number of confirmed COVID-19 cases. Lately, the published COVID data comprised new variables that may influence the accuracy of the oil/gold prices forecasting models including the Stringency index, Reproduction rate, Positive Rate, and Vaccinations. In this study, Deep Autoencoders are introduced and combined with the well-known approach: Pearson Correlation Coefficient, PCC, analysis in selecting the key features that affect the accuracy of the forecasting models of gold and oil prices with respect to COVID-19 pandemic. We have utilized a hybrid approach of PCC along with a 2-Stage Stacked Autoencoder, SA, to extract the latent features which are then submitted to Neural Network, NN, regression model. The NN regressor has been trained using the Bayesian Regularization-backpropagation algorithm which provides a good generalization for small noisy datasets. The hybrid approach has yielded the minimum MSE values of 8.97 × 10−3 and 5.356 × 10−2 on the oil/gold validation set, respectively. Compared to the existing approaches, the proposed approach has outperformed the ARIMA, ML based regression models in forecasting the oil/gold prices. In addition, the introduced framework has yielded lower Mean Absolute Error, MAE, than the Recurrent Neural Network, RNN, and the Principal Component Analysis, PCA, for dimension reduction. The retrieved results showed that the hybrid method produced more robust features by considering the relationship between the input features. |
| Author | Alkanhel, Reem Samee, Nagwan Abdel Alhussan, Amel Ali Atteia, Ghada AlEisa, Hussah Nasser |
| Author_xml | – sequence: 1 givenname: Nagwan Abdel orcidid: 0000-0001-5957-1383 surname: Samee fullname: Samee, Nagwan Abdel – sequence: 2 givenname: Ghada orcidid: 0000-0002-5462-595X surname: Atteia fullname: Atteia, Ghada – sequence: 3 givenname: Reem orcidid: 0000-0001-6395-4723 surname: Alkanhel fullname: Alkanhel, Reem – sequence: 4 givenname: Amel Ali orcidid: 0000-0001-7530-7961 surname: Alhussan fullname: Alhussan, Amel Ali – sequence: 5 givenname: Hussah Nasser surname: AlEisa fullname: AlEisa, Hussah Nasser |
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| Cites_doi | 10.2307/3003046 10.1016/j.oceaneng.2017.12.044 10.1108/EJMBE-10-2017-016 10.2139/ssrn.2275428 10.1016/j.physa.2018.09.120 10.1016/j.eswa.2017.05.024 10.2139/ssrn.3428095 10.1016/B978-0-12-373695-6.00001-6 10.1016/j.resourpol.2017.08.006 10.1016/j.measurement.2021.110085 10.1007/s12652-020-01682-z 10.1007/s40745-020-00256-2 10.1111/j.1540-6288.2010.00244.x 10.3390/en13102440 10.1016/j.eneco.2020.104790 10.1016/j.energy.2018.12.016 10.1016/S0042-6989(97)00169-7 10.1016/j.resourpol.2021.102217 10.5547/ISSN0195-6574-EJ-Vol27-No4-4 10.2307/1885568 10.1016/j.aasri.2014.05.013 10.3390/e23101251 10.1007/s42979-021-00724-3 10.1016/j.eneco.2015.02.018 10.1016/j.econlet.2020.109283 10.1080/14697688.2019.1633014 10.1162/089976600300015015 10.1109/EMBC.2018.8513683 10.1287/mnsc.2013.1756 10.1016/j.eswa.2018.03.002 10.1155/2021/7778605 10.1109/IJCNN.2010.5596602 10.1016/j.enpol.2009.08.020 10.12973/ejmste/77926 10.1016/j.irfa.2020.101496 10.1016/j.ijsu.2020.04.018 10.1016/j.energy.2014.12.074 10.1016/j.resourpol.2021.102061 10.1016/j.procs.2017.11.373 10.1007/978-3-642-38667-1_27 |
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| References | Cen (ref_17) 2019; 169 Baur (ref_4) 2010; 45 Moshiri (ref_12) 2006; 27 ref_11 Samuelson (ref_28) 1973; 4 Sharif (ref_8) 2020; 70 ref_19 Arai (ref_31) 2021; 12 Olshausen (ref_45) 1997; 37 ref_15 Yan (ref_9) 2021; 2021 ref_25 ref_24 Wu (ref_16) 2019; 516 Nicola (ref_6) 2020; 78 ref_22 Atteia (ref_29) 2021; 12 Sariev (ref_23) 2020; 20 Weng (ref_49) 2020; 11 He (ref_48) 2018; 27 Oh (ref_33) 2018; 150 ref_26 SenGupta (ref_38) 2021; 8 Shamshirband (ref_27) 2020; 14 Li (ref_34) 2021; 186 Smirnov (ref_46) 2014; 6 Soytas (ref_3) 2009; 37 ref_32 Zhao (ref_37) 2014; 211 ref_30 Gers (ref_39) 2000; 12 Kim (ref_18) 2018; 103 Singleton (ref_1) 2014; 60 Chen (ref_21) 2017; 122 Zhang (ref_13) 2015; 49 Vahidnia (ref_51) 2021; 2 Mensi (ref_5) 2021; 73 Wang (ref_20) 2020; 91 Atri (ref_47) 2021; 72 Bakas (ref_7) 2020; 193 Kristjanpoller (ref_14) 2017; 84 Bernanke (ref_2) 1983; 98 ref_44 ref_43 ref_42 He (ref_50) 2017; 54 ref_41 ref_40 Yu (ref_36) 2017; 13 Jabeur (ref_52) 2021; 937 Arfaoui (ref_10) 2017; 26 Hu (ref_35) 2015; 81 |
| References_xml | – volume: 4 start-page: 369 year: 1973 ident: ref_28 article-title: Proof That Properly Discounted Present Values of Assets Vibrate Randomly publication-title: Bell J. Econ. Manag. Sci. doi: 10.2307/3003046 – volume: 150 start-page: 48 year: 2018 ident: ref_33 article-title: Real-Time Forecasting of Wave Heights Using EOF—Wavelet—Neural Network Hybrid Model publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2017.12.044 – volume: 26 start-page: 278 year: 2017 ident: ref_10 article-title: Oil, Gold, US Dollar and Stock Market Interdependencies: A Global Analytical Insight publication-title: Eur. J. Manag. Bus. Econ. doi: 10.1108/EJMBE-10-2017-016 – ident: ref_22 doi: 10.2139/ssrn.2275428 – volume: 516 start-page: 114 year: 2019 ident: ref_16 article-title: Improved EEMD-Based Crude Oil Price Forecasting Using LSTM Networks publication-title: Phys. Stat. Mech. Appl. doi: 10.1016/j.physa.2018.09.120 – volume: 84 start-page: 290 year: 2017 ident: ref_14 article-title: Volatility of Main Metals Forecasted by a Hybrid ANN-GARCH Model with Regressors publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2017.05.024 – ident: ref_19 doi: 10.2139/ssrn.3428095 – ident: ref_32 doi: 10.1016/B978-0-12-373695-6.00001-6 – volume: 54 start-page: 9 year: 2017 ident: ref_50 article-title: Price Forecasting in the Precious Metal Market: A Multivariate EMD Denoising Approach publication-title: Resour. Policy doi: 10.1016/j.resourpol.2017.08.006 – ident: ref_42 – volume: 186 start-page: 110085 year: 2021 ident: ref_34 article-title: A Missing Sensor Measurement Data Reconstruction Framework Powered by Multi-Task Gaussian Process Regression for Dam Structural Health Monitoring Systems publication-title: Measurement doi: 10.1016/j.measurement.2021.110085 – volume: 11 start-page: 4101 year: 2020 ident: ref_49 article-title: Gold Price Forecasting Research Based on an Improved Online Extreme Learning Machine Algorithm publication-title: J. Ambient. Intell. Humaniz. Comput. doi: 10.1007/s12652-020-01682-z – volume: 8 start-page: 39 year: 2021 ident: ref_38 article-title: Refinements of Barndorff-Nielsen and Shephard Model: An Analysis of Crude Oil Price with Machine Learning publication-title: Ann. Data Sci. doi: 10.1007/s40745-020-00256-2 – volume: 27 start-page: 25 year: 2018 ident: ref_48 article-title: Crude Oil Prices Forecasting: Time Series vs. SVR Models publication-title: J. Int. Technol. Inf. Manag. – volume: 14 start-page: 805 year: 2020 ident: ref_27 article-title: Prediction of Significant Wave Height; Comparison between Nested Grid Numerical Model, and Machine Learning Models of Artificial Neural Networks, Extreme Learning and Support Vector Machines publication-title: Eng. Appl. Comput. Fluid Mech. – volume: 45 start-page: 217 year: 2010 ident: ref_4 article-title: Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold publication-title: Financ. Rev. doi: 10.1111/j.1540-6288.2010.00244.x – ident: ref_11 doi: 10.3390/en13102440 – ident: ref_41 – volume: 91 start-page: 104790 year: 2020 ident: ref_20 article-title: A Multi-Granularity Heterogeneous Combination Approach to Crude Oil Price Forecasting publication-title: Energy Econ. doi: 10.1016/j.eneco.2020.104790 – volume: 169 start-page: 160 year: 2019 ident: ref_17 article-title: Crude Oil Price Prediction Model with Long Short Term Memory Deep Learning Based on Prior Knowledge Data Transfer publication-title: Energy doi: 10.1016/j.energy.2018.12.016 – volume: 12 start-page: 310 year: 2021 ident: ref_31 article-title: Combined Non-Parametric and Parametric Classification Method Depending on Normality of PDF of Training Samples publication-title: Int. J. Adv. Comput. Sci. Appl. – volume: 37 start-page: 3311 year: 1997 ident: ref_45 article-title: Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by V1? publication-title: Vis. Res. doi: 10.1016/S0042-6989(97)00169-7 – volume: 73 start-page: 102217 year: 2021 ident: ref_5 article-title: Price-Switching Spillovers between Gold, Oil, and Stock Markets: Evidence from the USA and China during the COVID-19 Pandemic publication-title: Resour. Policy doi: 10.1016/j.resourpol.2021.102217 – volume: 27 start-page: 81 year: 2006 ident: ref_12 article-title: Forecasting Nonlinear Crude Oil Futures Prices publication-title: Energy J. doi: 10.5547/ISSN0195-6574-EJ-Vol27-No4-4 – volume: 98 start-page: 85 year: 1983 ident: ref_2 article-title: Irreversibility, Uncertainty, and Cyclical Investment publication-title: Q. J. Econ. doi: 10.2307/1885568 – volume: 12 start-page: 647 year: 2021 ident: ref_29 article-title: Evaluation of Using Parametric and Non-Parametric Machine Learning Algorithms for COVID-19 Forecasting publication-title: Int. J. Adv. Comput. Sci. Appl. – ident: ref_30 – volume: 6 start-page: 89 year: 2014 ident: ref_46 article-title: Comparison of Regularization Methods for ImageNet Classification with Deep Convolutional Neural Networks publication-title: AASRI Procedia doi: 10.1016/j.aasri.2014.05.013 – ident: ref_24 – ident: ref_26 doi: 10.3390/e23101251 – volume: 2 start-page: 335 year: 2021 ident: ref_51 article-title: A Deep Learning-Based Method for Forecasting Gold Price with Respect to Pandemics publication-title: SN Comput. Sci. doi: 10.1007/s42979-021-00724-3 – volume: 49 start-page: 649 year: 2015 ident: ref_13 article-title: A Novel Hybrid Method for Crude Oil Price Forecasting publication-title: Energy Econ. doi: 10.1016/j.eneco.2015.02.018 – ident: ref_40 – volume: 193 start-page: 109283 year: 2020 ident: ref_7 article-title: Commodity Price Volatility and the Economic Uncertainty of Pandemics publication-title: Econ. Lett. doi: 10.1016/j.econlet.2020.109283 – ident: ref_44 – volume: 20 start-page: 311 year: 2020 ident: ref_23 article-title: Bayesian Regularized Artificial Neural Networks for the Estimation of the Probability of Default publication-title: Quant. Financ. doi: 10.1080/14697688.2019.1633014 – volume: 12 start-page: 2451 year: 2000 ident: ref_39 article-title: Learning to Forget: Continual Prediction with LSTM publication-title: Neural Comput. doi: 10.1162/089976600300015015 – ident: ref_25 doi: 10.1109/EMBC.2018.8513683 – volume: 60 start-page: 300 year: 2014 ident: ref_1 article-title: Investor Flows and the 2008 Boom/Bust in Oil Prices publication-title: Manag. Sci. doi: 10.1287/mnsc.2013.1756 – volume: 103 start-page: 25 year: 2018 ident: ref_18 article-title: Forecasting the Volatility of Stock Price Index: A Hybrid Model Integrating LSTM with Multiple GARCH-Type Models publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2018.03.002 – volume: 2021 start-page: 1 year: 2021 ident: ref_9 article-title: Selection of Machine Learning Models for Oil Price Forecasting: Based on the Dual Attributes of Oil publication-title: Discret. Dyn. Nat. Soc. doi: 10.1155/2021/7778605 – ident: ref_15 doi: 10.1109/IJCNN.2010.5596602 – volume: 37 start-page: 5557 year: 2009 ident: ref_3 article-title: World Oil Prices, Precious Metal Prices and Macroeconomy in Turkey publication-title: Energy Policy doi: 10.1016/j.enpol.2009.08.020 – volume: 937 start-page: 1 year: 2021 ident: ref_52 article-title: Forecasting Gold Price with the XGBoost Algorithm and SHAP Interaction Values publication-title: Ann. Oper. Res. – volume: 13 start-page: 7893 year: 2017 ident: ref_36 article-title: Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting publication-title: Eurasia J. Math. Sci. Technol. Educ. doi: 10.12973/ejmste/77926 – volume: 70 start-page: 101496 year: 2020 ident: ref_8 article-title: COVID-19 Pandemic, Oil Prices, Stock Market, Geopolitical Risk and Policy Uncertainty Nexus in the US Economy: Fresh Evidence from the Wavelet-Based Approach publication-title: Int. Rev. Financ. Anal. doi: 10.1016/j.irfa.2020.101496 – volume: 78 start-page: 185 year: 2020 ident: ref_6 article-title: The Socio-Economic Implications of the Coronavirus Pandemic (COVID-19): A Review publication-title: Int. J. Surg. doi: 10.1016/j.ijsu.2020.04.018 – ident: ref_43 – volume: 81 start-page: 563 year: 2015 ident: ref_35 article-title: A Hybrid Technique for Short-Term Wind Speed Prediction publication-title: Energy doi: 10.1016/j.energy.2014.12.074 – volume: 72 start-page: 102061 year: 2021 ident: ref_47 article-title: imen The Impact of COVID-19 News, Panic and Media Coverage on the Oil and Gold Prices: An ARDL Approach publication-title: Resour. Policy doi: 10.1016/j.resourpol.2021.102061 – volume: 122 start-page: 300 year: 2017 ident: ref_21 article-title: Forecasting Crude Oil Prices: A Deep Learning Based Model publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2017.11.373 – volume: 211 start-page: 275 year: 2014 ident: ref_37 article-title: Forecasting Crude Oil Price with an Autoregressive Integrated Moving Average (ARIMA) Model publication-title: Adv. Intell. Syst. Comput. doi: 10.1007/978-3-642-38667-1_27 |
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