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

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.

[1]  Pradyot Ranjan Jena,et al.  Development and performance evaluation of a novel knowledge guided artificial neural network (KGANN) model for exchange rate prediction , 2015, J. King Saud Univ. Comput. Inf. Sci..

[2]  Yong Liu,et al.  The application of Shuffled Frog Leaping Algorithm to Wavelet Neural Networks for acoustic emission source location , 2014 .

[3]  Deming Lei,et al.  A shuffled frog-leaping algorithm for hybrid flow shop scheduling with two agents , 2015, Expert Syst. Appl..

[4]  P. K. Dash,et al.  An evolutionary hybrid Fuzzy Computationally Efficient EGARCH model for volatility prediction , 2016, Appl. Soft Comput..

[5]  Xia Li,et al.  A novel hybrid shuffled frog leaping algorithm for vehicle routing problem with time windows , 2015, Inf. Sci..

[6]  Morteza Jadidoleslam,et al.  Reliability constrained generation expansion planning by a modified shuffled frog leaping algorithm , 2015 .

[7]  Pradipta Kishore Dash,et al.  Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network: Evolutionary LENN , 2016, Int. J. Appl. Evol. Comput..

[8]  Xia Li,et al.  An improved shuffled frog-leaping algorithm with extremal optimisation for continuous optimisation , 2012, Inf. Sci..

[9]  Kin Keung Lai,et al.  Multistage RBF neural network ensemble learning for exchange rates forecasting , 2008, Neurocomputing.

[10]  Morteza Jadidoleslam,et al.  Application of Shuffled Frog Leaping Algorithm to Long Term Generation Expansion Planning , 2012 .

[11]  Kevin E Lansey,et al.  Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm , 2003 .

[12]  Archana Sarangi,et al.  A new training strategy for neural network using shuffled frog-leaping algorithm and application to channel equalization , 2014 .

[13]  Min-Rong Chen,et al.  Improved Shuffled Frog Leaping Algorithm and its multi-phase model for multi-depot vehicle routing problem , 2014, Expert Syst. Appl..

[14]  Muzaffar Eusuff,et al.  Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization , 2006 .

[15]  P. K. Dash,et al.  Stock price index movement classification using a CEFLANN with extreme learning machine , 2015, 2015 IEEE Power, Communication and Information Technology Conference (PCITC).

[16]  Ranjeeta Bisoi,et al.  Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach , 2017, J. King Saud Univ. Comput. Inf. Sci..

[17]  Svitlana Galeshchuk,et al.  Neural networks performance in exchange rate prediction , 2016, Neurocomputing.

[18]  Liping Xue,et al.  An Improved Shuffled Frog Leaping Algorithm with Comprehensive Learning for Continuous Optimization , 2013 .

[19]  Ganapati Panda,et al.  Efficient prediction of exchange rates with low complexity artificial neural network models , 2009, Expert Syst. Appl..

[20]  Tarun Kumar Sharma,et al.  Centroid Mutation Embedded Shuffled Frog-Leaping Algorithm , 2015 .

[21]  Donald E. Grierson,et al.  A modified shuffled frog-leaping optimization algorithm: applications to project management , 2007 .

[22]  Pradipta Kishore Dash,et al.  A self adaptive differential harmony search based optimized extreme learning machine for financial time series prediction , 2014, Swarm Evol. Comput..

[23]  Chao Wu,et al.  Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm , 2011, Knowl. Based Syst..

[24]  P. Dash,et al.  A hybrid stock trading framework integrating technical analysis with machine learning techniques , 2016 .