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Soft Computing

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06.03.2019 | Methodologies & Application

Sliding-window metaheuristic optimization-based forecast system for foreign exchange analysis

verfasst von: Jui-Sheng Chou, Thi Thu Ha Truong

Erschienen in: Soft Computing | Ausgabe 10/2019

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Abstract

The forecasting of exchange rates has become a challenging area of research that has attracted many researchers over recent years. This work presents a sliding-window metaheuristic optimization-based forecast (SMOF) system for one-step ahead forecasting. The proposed system is a graphical user interface, which is developed in the MATLAB environment and functions as a stand-alone application. The system integrates the novel firefly algorithm (FA), metaheuristic (Meta) intelligence, and least squares support vector regression (LSSVR), namely MetaFA-LSSVR, with a sliding-window approach. The MetaFA automatically tunes the hyperparameters of the LSSVR to construct an optimal sliding-window LSSVR prediction model. The optimization effectiveness of the MetaFA is verified using ten benchmark functions. Two case studies on the daily Canadian dollar-USD exchange rate (CAN/USD) and the 4-h closing EUR-USD rates (EUR/USD) were used to confirm the performance of the system, in which the mean absolute percentage errors are 0.2532% and 0.169%, respectively. The forecast system has an 89.8–99.7% greater predictive accuracy than prior work when applied to the currency pair CAN/USD. With respect to the EUR/USD exchange rate, the error rates obtained using the proposed system were 20.8–23.9% better than those obtained by the baseline sliding-window LSSVR model. Therefore, the SMOF system is potentially useful for decision-makers in financial markets.

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Akerkar R (2013) Big data computing. Chapman and Hall/CRC, Boca RatonCrossRef

Box G, Jenkins G (1970) Time series analysis, forecasting and control. Holden-Day, San FranciscoMATH

Braverman V, Ostrovsky R, Zaniolo C (2012) Optimal sampling from sliding windows. J Comput Syst Sci 78(1):260–272. https://doi.org/10.1016/j.jcss.2011.04.004 MathSciNetCrossRefMATH

Chen T-T, Lee S-J (2015) A weighted LS-SVM based learning system for time series forecasting. Inf Sci 299:99–116. https://doi.org/10.1016/j.ins.2014.12.031 MathSciNetCrossRefMATH

Chou J-S, Pham A-D (2015) Smart artificial firefly colony algorithm-based support vector regression for enhanced forecasting in civil engineering. Comput Aid Civil Infrastruct Eng 30(9):715–732. https://doi.org/10.1111/mice.12121 CrossRef

Chou J-S, Yang K-H, Pampang JP, Pham A-D (2015) Evolutionary metaheuristic intelligence to simulate tensile loads in reinforcement for geosynthetic-reinforced soil structures. Comput Geotech 66:1–15. https://doi.org/10.1016/j.compgeo.2015.01.001 CrossRef

Coelho LdS, Mariani VC (2013) Improved firefly algorithm approach applied to chiller loading for energy conservation. Energy Build 59:273–278. https://doi.org/10.1016/j.enbuild.2012.11.030 CrossRef

Feng H, Riabov AV, Turaga DS (2013) Real-time analysis and management of big time-series data. IBM J Res Dev 57(3/4):8:1–8:12CrossRef

Hafezi R, Shahrabi J, Hadavandi E (2015) A bat-neural network multi-agent system (BNNMAS) for stock price prediction: case study of DAX stock price. Appl Soft Comput 29:196–210. https://doi.org/10.1016/j.asoc.2014.12.028 CrossRef

He D, He C, Jiang L, Zhu H, Hu G (2001) Chaotic characteristics of a one-dimensional iterative map with infinite collapses. IEEE Trans Circuits Syst I Fundam Theory Appl 48(7):900–906MathSciNetCrossRefMATH

Hoang N-D, Pham A-D, Cao M-T (2014) A novel time series prediction approach based on a hybridization of least squares support vector regression and swarm intelligence. Appl Comput Intell Soft Comput. https://doi.org/10.1155/2014/754809

Hong W-C (2010) Application of chaotic ant swarm optimization in electric load forecasting. Energy Policy 38(10):5830–5839. https://doi.org/10.1016/j.enpol.2010.05.033 CrossRef

Hong W-C (2011) Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm. Energy 36(9):5568–5578. https://doi.org/10.1016/j.energy.2011.07.015 CrossRef

Hong W-C, Dong Y, Chen L-Y, Wei S-Y (2011) SVR with hybrid chaotic genetic algorithms for tourism demand forecasting. Appl Soft Comput 11(2):1881–1890. https://doi.org/10.1016/j.asoc.2010.06.003 CrossRef

Hsieh H-I, Lee T-P, Lee T-S (2011) A hybrid particle swarm optimization and support vector regression model for financial time series forecasting. Int J Bus Adm 2:48–56

Huang W, Nakamori Y, Wang S-Y (2005) Forecasting stock market movement direction with support vector machine. Comput Oper Res 32(10):2513–2522. https://doi.org/10.1016/j.cor.2004.03.016 CrossRefMATH

Jamil M, Yang X-S (2013) A literature survey of benchmark functions for global optimization problems. Int J Math Modell Numer Optim 4:150–194MATH

Jilin Z (2013) Dynamic analysis on trend of stock price based on elasticity coefficient model. Comput Modell New Technol 17(4):260–269

Khemchandani R, Jayadeva Chandra S (2009) Regularized least squares fuzzy support vector regression for financial time series forecasting. Expert Syst Appl 36(1):132–138. https://doi.org/10.1016/j.eswa.2007.09.035 CrossRef

Lahmiri S (2016) A variational mode decompoisition approach for analysis and forecasting of economic and financial time series. Expert Syst Appl 55:268–273CrossRef

Lu C-J, Lee T-S, Chiu C-C (2009) Financial time series forecasting using independent component analysis and support vector regression. Decis Support Syst 47(2):115–125. https://doi.org/10.1016/j.dss.2009.02.001 CrossRef

Min Z, Huanq T (2011) Short term load forecasting with least square support vector regression and PSO. In: Zhang J (ed) Communications in computer and information science, vol 228. Springer, Heidelberg, pp 124–132

Min S-H, Lee J, Han I (2006) Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Syst Appl 31(3):652–660. https://doi.org/10.1016/j.eswa.2005.09.070 CrossRef

Mundani R-P, Frisch J, Varduhn V, Rank E (2015) A sliding window technique for interactive high-performance computing scenarios. Adv Eng Softw 84:21–30. https://doi.org/10.1016/j.advengsoft.2015.02.003 CrossRef

Özorhan MO, Toroslu İH, Şehitoğlu OT (2017) A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms. Soft Comput 21(22):6653–6671CrossRef

Pai P-F, Hong W-C (2005) Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Convers Manag 46(17):2669–2688. https://doi.org/10.1016/j.enconman.2005.02.004 CrossRef

Singh P, Borah B (2013) High-order fuzzy-neuro expert system for time series forecasting. Knowl Based Syst 46:12–21. https://doi.org/10.1016/j.knosys.2013.01.030 CrossRef

Smith ST (2006) MATLAB advanced GUI development. Dog Ear Publishing, LLC, Indianapolis

Song H, Gao BZ, Lin VS (2013) Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system. Int J Forecast 29(2):295–310. https://doi.org/10.1016/j.ijforecast.2011.12.003 CrossRef

Surjanovic S and Bingham D (2013) Virtual library of simulation experiments: test functions and datasets. http://www.sfu.ca/~ssurjano/optimization.html. Accessed May 8 2016

Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300CrossRef

Suykens JAK, Gestel TV, Brabanter JD, Moor BD, Vandewalle J (2002) Least squares support vector machines. World Scientific, SingaporeCrossRefMATH

Tay FEH, Cao L (2001) Application of support vector machines in financial time series forecasting. Omega 29(4):309–317. https://doi.org/10.1016/S0305-0483(01)00026-3 CrossRef

Vapnik VN (1995) The nature of statistical learning theory. Springer, New YorkCrossRefMATH

Wang H, Hu D (2005) Comparison of SVM and LS-SVM for regression. Paper presented at the International Conference on Neural Networks and Brain, Beijing, China

Wang J, Jin S, Qin S, Jiang H (2014) Swarm Intelligence-based hybrid models for short-term power load prediction. Math Probl Eng. https://doi.org/10.1155/2014/712417 MathSciNetMATH

Wu C-H, Tzeng G-H, Goo Y-J, Fang W-C (2007) A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Syst Appl 32(2):397–408. https://doi.org/10.1016/j.eswa.2005.12.008 CrossRef

Yang X-S (2008) Firefly algorithm. Luniver Press, Bristol

Yang X-S (2013) Multiobjective firefly algorithm for continuous optimization. Eng Comput 29(2):175–184CrossRef

Yang X-S, Deb S (2009) Cuckoo search via Lévy flights. In: Proceedings of World Congress on Nature & Biologically Inspired Computing, India, 2009, IEEE

Yang J, Rivard H, Zmeureanu R (2005) On-line building energy prediction using adaptive artificial neural networks. Energy Build 37(12):1250–1259. https://doi.org/10.1016/j.enbuild.2005.02.005 CrossRef

Yang D, Li G, Cheng G (2007) On the efficiency of chaos optimization algorithms for global optimization. Chaos Solitons Fractals 34(4):1366–1375. https://doi.org/10.1016/j.chaos.2006.04.057 MathSciNetCrossRef

Zhiqiang G, Huaiqing W, Quan L (2013) Financial time series forecasting using LPP and SVM optimized by PSO. Soft Comput 17(5):805–818CrossRef

Titel: Sliding-window metaheuristic optimization-based forecast system for foreign exchange analysis
verfasst von: Jui-Sheng Chou
Thi Thu Ha Truong
Publikationsdatum: 06.03.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 10/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-03863-1

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