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An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction

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  • Dash, Rajashree

Abstract

Forecasting purchasing power of one currency with respect to another currency is always an interesting topic in the field of financial time series prediction. Despite the existence of several traditional and computational models for currency exchange rate forecasting, there is always a need for developing simpler and more efficient model, which will produce better prediction capability. In this paper, an evolutionary framework is proposed by using an improved shuffled frog leaping (ISFL) algorithm with a computationally efficient functional link artificial neural network (CEFLANN) for prediction of currency exchange rate. The model is validated by observing the monthly prediction measures obtained for three currency exchange data sets such as USD/CAD, USD/CHF, and USD/JPY accumulated within same period of time. The model performance is also compared with two other evolutionary learning techniques such as Shuffled frog leaping algorithm and Particle Swarm optimization algorithm. Practical analysis of results suggest that, the proposed model developed using the ISFL algorithm with CEFLANN network is a promising predictor model for currency exchange rate prediction compared to other models included in the study.

Suggested Citation

  • Dash, Rajashree, 2017. "An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 782-796.
    • Handle: RePEc:eee:phsmap:v:486:y:2017:i:c:p:782-796
    DOI: 10.1016/j.physa.2017.05.044
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    References listed on IDEAS

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    1. Rajashree Dash & Pradipta Kishore Dash, 2016. "Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network: Evolutionary LENN," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 7(1), pages 16-32, January.
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    Cited by:

    1. Fu, Sibao & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2019. "Evolutionary support vector machine for RMB exchange rate forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 692-704.
    2. Samuka Mohanty & Rajashree Dash, 2022. "Neural Network-Based Bitcoin Pricing Using a New Mutated Climb Monkey Algorithm with TOPSIS Analysis for Sustainable Development," Mathematics, MDPI, vol. 10(22), pages 1-23, November.
    3. Tarun Kumar Sharma & Divya Prakash, 2020. "Air pollution emissions control using shuffled frog leaping algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 332-339, April.
    4. Tripathi Manas & Kumar Saurabh & Inani Sarveshwar Kumar, 2021. "Exchange Rate Forecasting Using Ensemble Modeling for Better Policy Implications," Journal of Time Series Econometrics, De Gruyter, vol. 13(1), pages 43-71, January.

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