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Accessible Unlicensed Requires Authentication Published by De Gruyter December 1, 2020

Exchange Rate Forecasting Using Ensemble Modeling for Better Policy Implications

Manas Tripathi, Saurabh Kumar and Sarveshwar Kumar Inani

Abstract

This study aims to contribute in the area of foreign exchange forecasting. Exchange rate plays an essential role for the economic policy of a country. Due to the floating exchange rate regime, and ever-changing economic conditions, analysts have observed significant volatility in the exchange rates. However, exchange rate forecasting has been a challenging task before the analysts over the years. Various stakeholders such as the central bank, government, and investors try to maximize the returns and minimize the risk in their decision-making using exchange rate forecasting. The study aims to propose a novel ensemble technique to forecast daily exchange rates for the three most traded currency pairs (EUR/USD, GBP/USD, and JPY/USD). The ensemble technique combines the linear and non-linear time-series forecasting techniques (mean forecast, ARIMA, and neural network) with their most optimal weights. We have taken the data of more than seven years, and the results indicate that the proposed methodology could be an effective technique to forecast better as compared to the component models separately. The study has crucial economic and academic implications. The results derived from this study would be useful for policymakers, regulators, investors, speculators, and arbitrageurs.

JEL Classification: G15; G17; F31; C45

Corresponding author: Manas Tripathi, Assistant Professor, Management Information Systems Area, Indian Institute of Management Rohtak, Rohtak, India, E-mail:

Appendix 1

Calculation of price differences for EUR/USD (30-days testing period).

DateActual priceReturns (ARIMA)Returns (ensemble)Price (ARIMA)Price (ensemble)
01-Apr-161.1385−0.00032−0.00025071.1386371.138715
04-Apr-161.1386−9.6E-05−9.216E-051.1385281.13861
05-Apr-161.1374−5.9E-05−6.786E-051.138461.138532
06-Apr-161.143−0.00014−0.00012471.1382951.13839
07-Apr-161.1386−0.00011−0.00010091.1381711.138275
08-Apr-161.1406−0.0001−9.719E-051.1380531.138165
11-Apr-161.1412−0.00012−0.00010621.137921.138044
12-Apr-161.1395−0.00011−0.00010241.1377941.137928
13-Apr-161.1281−0.00011−0.00010181.1376681.137812
14-Apr-161.1262−0.00011−0.00010321.137541.137694
15-Apr-161.1295−0.00011−0.00010261.1374131.137577
18-Apr-161.1322−0.00011−0.00010251.1372861.137461
19-Apr-161.1375−0.00011−0.00010281.1371591.137344
20-Apr-161.133−0.00011−0.00010271.1370321.137227
21-Apr-161.1301−0.00011−0.00010271.1369051.13711
22-Apr-161.1239−0.00011−0.00010271.1367781.136994
25-Apr-161.1274−0.00011−0.00010271.1366511.136877
26-Apr-161.1318−0.00011−0.00010271.1365241.13676
27-Apr-161.1322−0.00011−0.00010271.1363981.136643
28-Apr-161.1325−0.00011−0.00010271.1362711.136527
29-Apr-161.1441−0.00011−0.00010271.1361441.13641
02-May-161.1516−0.00011−0.00010271.1360171.136293
03-May-161.1508−0.00011−0.00010271.135891.136177
04-May-161.1486−0.00011−0.00010271.1357631.13606
05-May-161.1404−0.00011−0.00010271.1356361.135943
06-May-161.1421−0.00011−0.00010271.1355091.135827
09-May-161.1402−0.00011−0.00010271.1353831.13571
10-May-161.1386−0.00011−0.00010271.1352561.135594
11-May-161.1444−0.00011−0.00010271.1351291.135477
12-May-161.138−0.00011−0.00010271.1350021.13536

References

Adhikari, R., and R. K. Agrawal. 2014. “A Combination of Artificial Neural Network and Random Walk Models for Financial Time Series Forecasting.” Neural Computing & Applications 24 (6): 1441–9, https://doi.org/10.1007/s00521-013-1386-y.Search in Google Scholar

Alameer, Z., M. Abd Elaziz, A. A. Ewees, H. Ye, and Z. Jianhua. 2019. “Forecasting Gold Price Fluctuations Using Improved Multilayer Perceptron Neural Network and Whale Optimization Algorithm.” Resources Policy 61 (June): 250–60, https://doi.org/10.1016/j.resourpol.2019.02.014.Search in Google Scholar

Alfaro, E., N. García, M. Gámez, and D. Elizondo. 2008. “Bankruptcy Forecasting: An Empirical Comparison of AdaBoost and Neural Networks.” Decision Support Systems 45 (1): 110–22, https://doi.org/10.1016/j.dss.2007.12.002.Search in Google Scholar

Atiya, A. F. 2001. “Bankruptcy Prediction for Credit Risk Using Neural Networks: A Survey and New Results.” IEEE Transactions on Neural Networks 12 (4): 929–35, https://doi.org/10.1109/72.935101.Search in Google Scholar

Auboin, M., and M. Ruta. 2012. “The Relationship between Exchange Rates and International Trade: A Literature Review.” CESifo Working Paper Series 3868. Munich: CESifo Group. Also available at .Search in Google Scholar

Balkin, S. D., and J. K. Ord. 2000. “Automatic Neural Network Modeling for Univariate Time Series.” International Journal of Forecasting 16 (4): 509–15. The M3-Competition, https://doi.org/10.1016/S0169-2070(00)00072-8.Search in Google Scholar

Baron, D. P. 1976. “Flexible Exchange Rates, Forward Markets, and the Level of Trade.” The American Economic Review 66 (3): 253–66.Search in Google Scholar

Beckmann, J., and R. Schüssler. 2016. “Forecasting Exchange Rates under Parameter and Model Uncertainty.” Journal of International Money and Finance 60 (February): 267–88, https://doi.org/10.1016/j.jimonfin.2015.07.001.Search in Google Scholar

Bowden, N., and J. E. Payne. 2008. “Short Term Forecasting of Electricity Prices for MISO Hubs: Evidence from ARIMA-EGARCH Models.” Energy Economics 30 (6): 3186–97. Technological Change and the Environment, https://doi.org/10.1016/j.eneco.2008.06.003.Search in Google Scholar

Box, G. E. P., and G. M. Jenkins. 1976. Time Series Analysis, Control, and Forecasting. San Francisco, CA: Holden Day.Search in Google Scholar

Broll, U., and B. Eckwert. 1999. “Exchange Rate Volatility and International Trade.” Southern Economic Journal 66 (1): 178–85, https://doi.org/10.2307/1060843.Search in Google Scholar

Buckley, P., and F. O’Brien. 2017. “The Effect of Malicious Manipulations on Prediction Market Accuracy.” Information Systems Frontiers 19 (3): 611–23, https://doi.org/10.1007/s10796-015-9617-7.Search in Google Scholar

Byrne, J. P., J. Darby, and R. MacDonald. 2008. “US Trade and Exchange Rate Volatility: A Real Sectoral Bilateral Analysis.” Journal of Macroeconomics 30 (1): 238–59.Search in Google Scholar

Chen, A-S., M. T. Leung, and H. Daouk. 2003. “Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index.” Computers & Operations Research 30 (6): 901–23. Operation Research in Emerging Economics, https://doi.org/10.1016/S0305-0548(02)00037-0.Search in Google Scholar

Chen, Y., and G. Zhang. 2013. “Exchange Rates Determination Based on Genetic Algorithms Using Mendel’s Principles: Investigation and Estimation under Uncertainty.” Information Fusion 14 (3): 327–33, https://doi.org/10.1016/j.inffus.2011.12.003.Search in Google Scholar

Choi, T-M., Y. Yu, and K-F. Au. 2011. “A Hybrid SARIMA Wavelet Transform Method for Sales Forecasting.” Decision Support Systems 51 (1): 130–40, https://doi.org/10.1016/j.dss.2010.12.002.Search in Google Scholar

Clark, P. B. 1973. “Uncertainty, Exchange Risk, and the Level of International Trade.” Economic Inquiry 11 (3): 302–13, https://doi.org/10.1111/j.1465-7295.1973.tb01063.x.Search in Google Scholar

Coussement, K., and K. W. De Bock. 2013. “Customer Churn Prediction in the Online Gambling Industry: The Beneficial Effect of Ensemble Learning.” Journal of Business Research, Advancing Research Methods in Marketing 66 (9): 1629–36, https://doi.org/10.1016/j.jbusres.2012.12.008.Search in Google Scholar

Cushman, D. O. 1983. “The Effects of Real Exchange Rate Risk on International Trade.” Journal of International Economics 15 (1–2): 45–63, https://doi.org/10.1016/0022-1996(83)90041-7.Search in Google Scholar

Dash, R. 2017. “An Improved Shuffled Frog Leaping Algorithm Based Evolutionary Framework for Currency Exchange Rate Prediction.” Physica A: Statistical Mechanics and its Applications 486 (November): 782–96, https://doi.org/10.1016/j.physa.2017.05.044.Search in Google Scholar

Dash, R., and P. K. Dash. 2016. “An Evolutionary Hybrid Fuzzy Computationally Efficient EGARCH Model for Volatility Prediction.” Applied Soft Computing 45 (August): 40–60, https://doi.org/10.1016/j.asoc.2016.04.014.Search in Google Scholar

Dornbusch, R., S. Fischer, and P. A. Samuelson. 1977. “Comparative Advantage, Trade, and Payments in a Ricardian Model with a Continuum of Goods.” The American Economic Review 67 (5): 823–39.Search in Google Scholar

Dunis, C. L., J. Laws, and U. Schilling. 2012. “Currency Trading in Volatile Markets: Did Neural Networks Outperform for the EUR/USD during the Financial Crisis 2007–2009?” Journal of Derivatives and Hedge Funds 18 (1): 2–41, https://doi.org/10.1057/jdhf.2011.31.Search in Google Scholar

Garcin, M. 2017. “Estimation of Time-dependent Hurst Exponents with Variational Smoothing and Application to Forecasting Foreign Exchange Rates.” Physica A: Statistical Mechanics and its Applications 483 (October): 462–79, https://doi.org/10.1016/j.physa.2017.04.122.Search in Google Scholar

Ghazali, R., A. J. Hussain, N. M. Nawi, and B. Mohamad. 2009. “Non-Stationary and Stationary Prediction of Financial Time Series Using Dynamic Ridge Polynomial Neural Network.” Neurocomputing 72 (10–12): 2359–67. Lattice Computing and Natural Computing (JCIS 2007)/Neural Networks in Intelligent Systems Designn (ISDA 2007), https://doi.org/10.1016/j.neucom.2008.12.005.Search in Google Scholar

Grudnitski, G., and L. Osburn. 1993. “Forecasting S&P and Gold Futures Prices: An Application of Neural Networks.” Journal of Futures Markets 13 (6): 631–43, https://doi.org/10.1002/fut.3990130605.Search in Google Scholar

Hann, T. H., and E. Steurer. 1996. “Much Ado about Nothing? Exchange Rate Forecasting: Neural Networks vs. Linear Models Using Monthly and Weekly Data.” Neurocomputing 10 (4): 323–39. Financial Applications, https://doi.org/10.1016/0925-2312(95)00137-9.Search in Google Scholar

Heckscher, E. F., and B. Gotthard Ohlin. 1991. Heckscher-Ohlin Trade Theory. Cambridge: The MIT Press.Search in Google Scholar

Herwartz, H. 2017. “Stock Return Prediction under GARCH — An Empirical Assessment.” International Journal of Forecasting 33 (3): 569–80, https://doi.org/10.1016/j.ijforecast.2017.01.002.Search in Google Scholar

Hu, Z., W. Liu, J. Bian, X. Liu, and T-Y. Liu. 2018. “Listening to Chaotic Whispers: A Deep Learning Framework for News-Oriented Stock Trend Prediction.” In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 261–9 WSDM ’18. New York, NY, USA: Association for Computing Machinery, https://doi.org/10.1145/3159652.3159690.Search in Google Scholar

Huang, Z., H. Chen, C-J. Hsu, W-H. Chen, and S. Wu. 2004. “Credit Rating Analysis with Support Vector Machines and Neural Networks: A Market Comparative Study.” Decision Support Systems 37 (4): 543–58. Data mining for financial decision making, https://doi.org/10.1016/S0167-9236(03)00086-1.Search in Google Scholar

Hussain, A. J., A. Knowles, P. J. G. Lisboa, and W. El-Deredy. 2008. “Financial Time Series Prediction Using Polynomial Pipelined Neural Networks.” Expert Systems with Applications 35 (3): 1186–99 https://doi.org/10.1016/j.eswa.2007.08.038.Search in Google Scholar

Inani, S. K., M. Tripathi, and S. Kumar. 2016. “Does Artificial Neural Network Forecast Better for Excessively Volatile Currency Pairs?” Journal of Prediction Markets 10 (2): 47–61.Search in Google Scholar

Ince, H., and T. B. Trafalis. 2006. “A Hybrid Model for Exchange Rate Prediction.” Decision Support Systems 42 (2): 1054–62, https://doi.org/10.1016/j.dss.2005.09.001.Search in Google Scholar

Jayme, F. G.Jr. 2001. “Notes on Trade and Growth.” Texto Para Discussão, no.166.Search in Google Scholar

Jilani, T. A., and S. M. A. Burney. 2008. “A Refined Fuzzy Time Series Model for Stock Market Forecasting.” Physica A: Statistical Mechanics and its Applications 387 (12): 2857–62, https://doi.org/10.1016/j.physa.2008.01.099.Search in Google Scholar

Kayal, P., and S. Maheswaran. 2016. “Is USD-INR Really an Excessively Volatile Currency Pair?” Journal of Quantitative Economics (August): 1–14, https://doi.org/10.1007/s40953-016-0054-3.Search in Google Scholar

Khansa, L., and D. Liginlal. 2011. “Predicting Stock Market Returns from Malicious Attacks: A Comparative Analysis of Vector Autoregression and Time-Delayed Neural Networks.” Decision Support Systems 51 (4): 745–59, https://doi.org/10.1016/j.dss.2011.01.010.Search in Google Scholar

Khashei, M. 2011. “A Novel Hybridization of Artificial Neural Networks and ARIMA Models for Time Series Forecasting.” Applied Soft Computing 11 (2): 2664–75. The Impact of Soft Computing for the Progress of Artificial Intelligence, https://doi.org/10.1016/j.asoc.2010.10.015.Search in Google Scholar

Khashei, M., and M. Bijari. 2010. “An Artificial Neural Network (p, d,q) Model for Timeseries Forecasting.” Expert Systems with Applications 37 (1): 479–89, https://doi.org/10.1016/j.eswa.2009.05.044.Search in Google Scholar

Khashei, M., H. Reza Seyed, and B. Mehdi. 2008. “A New Hybrid Artificial Neural Networks and Fuzzy Regression Model for Time Series Forecasting.” Fuzzy Sets and Systems, Theme: Fuzzy Models and Approximation Methods 159 (7): 769–86, https://doi.org/10.1016/j.fss.2007.10.011.Search in Google Scholar

Khashei, M., M. Bijari, and G. A. Raissi Ardali. 2009. “Improvement of Auto-Regressive Integrated Moving Average Models Using Fuzzy Logic and Artificial Neural Networks (ANNs).” Neurocomputing 72 (4–6): 956–67. Brain Inspired Cognitive Systems (BICS 2006) / Interplay Between Natural and Artificial Computation (IWINAC 2007), https://doi.org/10.1016/j.neucom.2008.04.017.Search in Google Scholar

Korczak, J., and M. Hernes. 2017. “Deep Learning for Financial Time Series Forecasting in A-Trader System.” In Annals of Computer Science and Information Systems, Vol. 11, 905–12. Also available at .Search in Google Scholar

Kumar, P. R., and V. Ravi. 2007. “Bankruptcy Prediction in Banks and Firms via Statistical and Intelligent Techniques – A Review.” European Journal of Operational Research 180 (1): 1–28, https://doi.org/10.1016/j.ejor.2006.08.043.Search in Google Scholar

Lahmiri, S. 2017. “Modeling and Predicting Historical Volatility in Exchange Rate Markets.” Physica A: Statistical Mechanics and its Applications 471 (April): 387–95, https://doi.org/10.1016/j.physa.2016.12.061.Search in Google Scholar

Lam, M. 2004. “Neural Network Techniques for Financial Performance Prediction: Integrating Fundamental and Technical Analysis.” Decision Support Systems 37 (4): 567–81, https://doi.org/10.1016/s0167-9236(03)00088-5.Search in Google Scholar

Lee, J. K., and S. Y. Chang. 1998. “Judgemental Adjustment in Time Series Forecasting Using Neural Networks.” Decision Support Systems 22 (2): 135, https://doi.org/10.1016/S0167-9236(97)00050-X.Search in Google Scholar

Luxhøj, J. T., J. O. Riis, and B. Stensballe. 1996. “A Hybrid Econometric—Neural Network Modeling Approach for Sales Forecasting.” International Journal of Production Economics 43 (2): 175–92, https://doi.org/10.1016/0925-5273(96)00039-4.Search in Google Scholar

Makridakis, S. 1989. “Why Combining Works?” International Journal of Forecasting 5 (4): 601–3, https://doi.org/10.1016/0169-2070(89)90017-4.Search in Google Scholar

Meese, R. A., and K. Rogoff. 1983. “Empirical Exchange Rate Models of the Seventies: Do They Fit Out of Sample?” Journal of International Economics 14 (1): 3–24, https://doi.org/10.1016/0022-1996(83)90017-X.Search in Google Scholar

Moosa, I. A., and J. J. Vaz. 2016. “Cointegration, Error Correction and Exchange Rate Forecasting.” Journal of International Financial Markets, Institutions and Money 44 (September): 21–34, https://doi.org/10.1016/j.intfin.2016.04.007.Search in Google Scholar

Niu, T., J. Wang, H. Lu, W. Yang, and P. Du. 2020. “Developing a Deep Learning Framework with Two-Stage Feature Selection for Multivariate Financial Time Series Forecasting.” Expert Systems with Applications 148 (June): 113237, https://doi.org/10.1016/j.eswa.2020.113237.Search in Google Scholar

Pai, P-F., and C-S. Lin. 2005. “A Hybrid ARIMA and Support Vector Machines Model in Stock Price Forecasting.” Omega 33 (6): 497–505, https://doi.org/10.1016/j.omega.2004.07.024.Search in Google Scholar

Palm, F. C., and A. Zellner. 1992. “To Combine or Not to Combine? Issues of Combining Forecasts.” Journal of Forecasting 11 (8): 687–701, https://doi.org/10.1002/for.3980110806.Search in Google Scholar

Panda, C., and V. Narasimhan. 2007. “Forecasting Exchange Rate Better with Artificial Neural Network.” Journal of Policy Modeling 29 (2): 227–36, https://doi.org/10.1016/j.jpolmod.2006.01.005.Search in Google Scholar

Pasley, A., and J. Austin. 2004. “Distribution Forecasting of High Frequency Time Series.” Decision Support Systems 37 (4): 501–13, https://doi.org/10.1016/s0167-9236(03)00083-6.Search in Google Scholar

Péridy, N. 2003. “Exchange Rate Volatility, Sectoral Trade, and the Aggregation Bias.” Review of World Economics 139 (3): 389–418.Search in Google Scholar

Pradeepkumar, D., and V. Ravi. 2017. “Forecasting Financial Time Series Volatility Using Particle Swarm Optimization Trained Quantile Regression Neural Network.” Applied Soft Computing 58 (September): 35–52, https://doi.org/10.1016/j.asoc.2017.04.014.Search in Google Scholar

Prakash, A. 2012. “Major Episodes of Volatility in the Indian Foreign Exchange Market in the Last Two Decades (1993-2013): Central Bank’s Response.” Reserve Bank of India Occasional Papers 33 (1 & 2): 166–99.Search in Google Scholar

Prusa, J. D., R. T. Sagul, and T. M. Khoshgoftaar. 2018. “Extracting Knowledge from Technical Reports for the Valuation of West Texas Intermediate Crude Oil Futures.” Information Systems Frontiers 21: 109–23, https://doi.org/10.1007/s10796-018-9859-2.Search in Google Scholar

Qureshi, S., I. U. Rehman, and F. Qureshi. 2018. “Does Gold Act as a Safe Haven against Exchange Rate Fluctuations? The Case of Pakistan Rupee.” Journal of Policy Modeling 40 (4): 685–708, https://doi.org/10.1016/j.jpolmod.2018.02.005.Search in Google Scholar

Rodríguez-González, A., R. Colomo-Palacios, F. Guldris-Iglesias, J. M. Gómez-Berbís, and A. García-Crespo. 2012. “FAST: Fundamental Analysis Support for Financial Statements. Using Semantics for Trading Recommendations.” Information Systems Frontiers 14 (5): 999–1017, https://doi.org/10.1007/s10796-011-9321-1.Search in Google Scholar

Rodrik, D. 2007. “The Real Exchange Rate and Economic Growth: Theory and Evidence.” Kennedy School of Government Manuscript.” In Center for Global Development. Cambridge, MACambridge: John F. Kennedy School of Government, Harvard University.Search in Google Scholar

Sermpinis, G., C. Dunis, J. Laws, and C. Stasinakis. 2012. “Forecasting and Trading the EUR/USD Exchange Rate with Stochastic Neural Network Combination and Time-Varying Leverage.” Decision Support Systems 54 (1): 316–29, https://doi.org/10.1016/j.dss.2012.05.039.Search in Google Scholar

Sermpinis, G., C. Stasinakis, K. Theofilatos, and A. Karathanasopoulos. 2015. “Modeling, Forecasting and Trading the EUR Exchange Rates with Hybrid Rolling Genetic Algorithms—Support Vector Regression Forecast Combinations.” European Journal of Operational Research 247 (3): 831–46, https://doi.org/10.1016/j.ejor.2015.06.052.Search in Google Scholar

Shen, F., J. Chao, and J. Zhao. 2015. “Forecasting Exchange Rate Using Deep Belief Networks and Conjugate Gradient Method.” Neurocomputing 167 (November): 243–53, https://doi.org/10.1016/j.neucom.2015.04.071.Search in Google Scholar

Shmueli, G., and K. Otto. 2010. “Predictive Analytics in Information Systems Research.” Robert H. Smith School Research Paper No. RHS, 06–138. Also available at .Search in Google Scholar

Srinivasan, T. N., and J. Bhagwati. 2001. “Outward-Orientation and Development: Are Revisionists Right?” In Trade, Development and Political Economy, Vol. 3–26: London: Palgrave Macmillan.Search in Google Scholar

Sun, S., S. Wang, Y. Wei, and G. Zhang. 2018. “A Clustering-Based Nonlinear Ensemble Approach for Exchange Rates Forecasting.” IEEE Transactions on Systems, Man, and Cybernetics: Systems 50 (6): 2284–92.Search in Google Scholar

Taskaya-Temizel, T., and M. C. Casey. 2005. “A Comparative Study of Autoregressive Neural Network Hybrids.” Neural Networks 18 (5–6): 781–89. IJCNN 2005, https://doi.org/10.1016/j.neunet.2005.06.003.Search in Google Scholar

Tkáč, M., and R. Verner. 2016. “Artificial Neural Networks in Business: Two Decades of Research.” Applied Soft Computing 38 (January): 788–804, https://doi.org/10.1016/j.asoc.2015.09.040.Search in Google Scholar

Wedding, D. K., and K. J. Cios. 1996. “Time Series Forecasting by Combining RBF Networks, Certainty Factors, and the Box-Jenkins Model.” Neurocomputing 10 (2): 149–68. Financial Applications, Part I, https://doi.org/10.1016/0925-2312(95)00021-6.Search in Google Scholar

Wezel, M. V., and R. Potharst. 2007. “Improved Customer Choice Predictions Using Ensemble Methods.” European Journal of Operational Research 181 (1): 436–52, https://doi.org/10.1016/j.ejor.2006.05.029.Search in Google Scholar

Wilson, R. L., and R. Sharda. 1994. “Bankruptcy Prediction Using Neural Networks.” Decision Support Systems 11 (5): 545–57, https://doi.org/10.1016/0167-9236(94)90024-8.Search in Google Scholar

Yu, L., S. Wang, and K. K. Lai. 2005. “A Novel Nonlinear Ensemble Forecasting Model Incorporating GLAR and ANN for Foreign Exchange Rates.” Computers & Operations Research 32 (10): 2523–41. Applications of Neural Networks, https://doi.org/10.1016/j.cor.2004.06.024.Search in Google Scholar

Yu, L., H. Chen, S. Wang, and K. K. Lai. 2008. “Evolving Least Squares Support Vector Machines for Stock Market Trend Mining.” IEEE Transactions on Evolutionary Computation 13 (1): 87–102.Search in Google Scholar

Yu, L., K. K. Lai, and S. Wang. 2008. “Multistage RBF Neural Network Ensemble Learning for Exchange Rates Forecasting.” Neurocomputing 71 (16): 3295–302. Advances in Neural Information Processing (ICONIP 2006)/Brazilian Symposium on Neural Networks (SBRN 2006), https://doi.org/10.1016/j.neucom.2008.04.029.Search in Google Scholar

Zhang, G. P. 2003. “Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model.” Neurocomputing 50 (January): 159–75, https://doi.org/10.1016/S0925-2312(01)00702-0.Search in Google Scholar

Zhang, G. P., and M. Qi. 2005. “Neural Network Forecasting for Seasonal and Trend Time Series.” European Journal of Operational Research 160 (2): 501–14. Decision Support Systems in the Internet Age, https://doi.org/10.1016/j.ejor.2003.08.037.Search in Google Scholar

Zhang, G., B. E. Patuwo, and M. Y. Hu. 1998. “Forecasting with Artificial Neural Networks: The State of the Art.” International Journal of Forecasting 14 (1): 35–62, https://doi.org/10.1016/S0169-2070(97)00044-7.Search in Google Scholar

Received: 2020-04-10
Accepted: 2020-10-19
Published Online: 2020-12-01

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