Neural Computing and Applications

, Volume 31, Issue 11, pp 7071–7094 | Cite as

A hybridized ELM using self-adaptive multi-population-based Jaya algorithm for currency exchange prediction: an empirical assessment

  • Smruti Rekha DasEmail author
  • Debahuti Mishra
  • Minakhi Rout
Original Article


This paper proposes a hybridized machine-learning framework called Extreme Learning Machine using self-adaptive multi-population-based Jaya algorithm for forecasting the currency exchange value. This learning technique attempts to take the advantages of generalization ability of Extreme Learning Machines (ELMs) along with the multi-population search scheme of Jaya optimization technique. This model can very well forecast the exchange price of USD–INR and USD–EURO based on statistical measures, technical indicators and combination of both measures over a time frame varying from 1 day to 1 month ahead. Proposed model has been compared with original ELM and ELM-Jaya along with technical analysis method such as discrete wavelet neural network optimized with self-adaptive multi-population-based Jaya and the comparison of different performance measures like MAPE, Theil’s U, ARV and MAE reveal that ELM using self-adaptive multi-population-based Jaya hybrid models possesses superior compared to the rest predictive models. Comparison of different features demonstrates technical indicators outperform other two features such as statistical measures and combination of both technical indicators and statistical measures.


Extreme learning machine Jaya Self-adaptive multi-population-based Jaya algorithm Discrete wavelet neural network 


Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflict of interest.


  1. 1.
    Lisi F, Schiavo RA (1999) A comparison between neural networks and chaotic models for exchange rate prediction. Comput Stat Data Anal 30(1):87–102zbMATHGoogle Scholar
  2. 2.
    Ince H, Trafalis TB (2006) A hybrid model for exchange rate prediction. Decis Support Syst 42(2):1054–1062Google Scholar
  3. 3.
    Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501Google Scholar
  4. 4.
    Das SR, Mishra D, Rout M (2017) A hybridized ELM-Jaya forecasting model for currency exchange prediction. J King Saud Univ Comput Inf Sci.
  5. 5.
    Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34Google Scholar
  6. 6.
    Rao RV, Saroj A (2017) A self-adaptive multi-population based Jaya algorithm for engineering optimization. Swarm Evolut Comput 37:1–26. Google Scholar
  7. 7.
    Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407(1):28–40Google Scholar
  8. 8.
    Lahmiri S (2014) An exploration of backpropagation numerical algorithms in modeling US exchange rates. In: Handbook of research on organizational transformations through big data analytics.
  9. 9.
    Kuan C-M, Liu T (1995) Forecasting exchange rates using feedforward and recurrent neural networks. J Appl Econom 10(4):347–364. Google Scholar
  10. 10.
    Zhang G, Hu MY (1998) Neural network forecasting of the British pound/US dollar exchange rate. Omega 26(4):495–506Google Scholar
  11. 11.
    Panda Chakradhara, Narasimhan V (2007) Forecasting exchange rate better with artificial neural network. J Policy Model 29(2):227–236Google Scholar
  12. 12.
    Hann TH, Steurer E (1996) Much ado about nothing? Exchange rate forecasting: neural networks vs. linear models using monthly and weekly data. Neurocomputing 10(4):323–339zbMATHGoogle Scholar
  13. 13.
    Pai P-F, Lin C-S, Hong W-C, Chen C-T (2006) A hybrid support vector machine regression for exchange rate prediction. Int J Inf Manag Sci 17(2):19–32zbMATHGoogle Scholar
  14. 14.
    Cao D-Z, Pang S-L, Bai Y-H (2005) Forecasting exchange rate using support vector machines. In: Proceedings of 2005 international conference on machine learning and cybernetics, 2005, vol 6. IEEE, pp. 3448–3452Google Scholar
  15. 15.
    Refenes AN, Azema-Barac M, Chen L, Karoussos SA (1993) Currency exchange rate prediction and neural network design strategies. Neural Comput Appl 1(1):46–58Google Scholar
  16. 16.
    Gencay R (1999) Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules. J Int Econ 47(1):91–107Google Scholar
  17. 17.
    Balabin RM, Safieva RZ, Lomakina EI (2008) Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra. Chemometr Intell Lab Syst 93(1):58–62Google Scholar
  18. 18.
    Zhong H, Zhang J, Gao M, Zheng J, Li G, Chen L (2001) The discrete wavelet neural network and its application in oscillographic chronopotentiometric determination. Chemometr Intell Lab Syst 59(1):67–74Google Scholar
  19. 19.
    Sun Z-L, Choi T-M, Au K-F, Yu Y (2008) Sales forecasting using extreme learning machine with applications in fashion retailing. Decis Support Syst 46(1):411–419Google Scholar
  20. 20.
    Zong W, Huang G-B (2011) Face recognition based on extreme learning machine. Neurocomputing 74(16):2541–2551Google Scholar
  21. 21.
    Wan C, Xu Z, Pinson P, Dong ZY, Wong KP (2014) Probabilistic forecasting of wind power generation using extreme learning machine. IEEE Trans Power Syst 29(3):1033–1044Google Scholar
  22. 22.
    Li X, Xie H, Wang R, Cai Y, Cao J, Wang F, Min H, Deng X (2016) Empirical analysis: stock market prediction via extreme learning machine. Neural Comput Appl 27(1):67–78Google Scholar
  23. 23.
    Dash R, Dash PK, Bisoi R (2014) A self adaptive differential harmony search based optimized extreme learning machine for financial time series prediction. Swarm Evolut Comput 19:25–42Google Scholar
  24. 24.
    Dash R, Dash PK (2015) Stock price index movement classification using a CEFLANN with extreme learning machine. In: Power, communication and information technology conference (PCITC). IEEE, pp 22–28Google Scholar
  25. 25.
    Rao RV, Waghmare GG (2017) A new optimization algorithm for solving complex constrained design optimization problems. Eng Optim 49(1):60–83Google Scholar
  26. 26.
    Rao RV, Rai DP, Balic J (2016) Surface grinding process optimization using Jaya algorithm. In: Computational intelligence in data mining—volume 2. Springer, pp 487–495Google Scholar
  27. 27.
    Rao RV (2016) Teaching–learning-based optimization algorithm. In: Teaching learning based optimization algorithm. Springer, pp 9–39Google Scholar
  28. 28.
    Rao R (2016) Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Decis Sci Lett 5(1):1–30Google Scholar
  29. 29.
    Rao RV, Saroj A (2016) Multi-objective design optimization of heat exchangers using elitist-Jaya algorithm. Energy Syst 2016:1–37Google Scholar
  30. 30.
    Rao RV, Saroj A (2017) Economic optimization of shell-and-tube heat exchanger using Jaya algorithm with maintenance consideration. Appl Therm Eng 116:473–487Google Scholar
  31. 31.
    Mishra S, Ray PK (2016) Power quality improvement using photovoltaic fed DSTATCOM based on JAYA optimization. IEEE Trans Sustain Energy 7(4):1672–1680Google Scholar
  32. 32.
    Rao RV, More KC, Taler J, Ocłoń P (2016) Dimensional optimization of a micro-channel heat sink using Jaya algorithm. Appl Therm Eng 103:572–582Google Scholar
  33. 33.
    Rao RV, Rai DP (2017) Optimisation of welding processes using quasi-oppositional-based Jaya algorithm. J Exp Theor Artif Intell 29(5):1099–1117Google Scholar
  34. 34.
    Rao RV, Rai DP (2017) Optimization of submerged arc welding process parameters using quasi-oppositional based Jaya algorithm. J Mech Sci Technol 31(5):2513–2522Google Scholar
  35. 35.
    Rao RV, Saroj A, Ocloń P, Taler J, Taler D (2017) Single-and multi-objective design optimization of plate-fin heat exchangers using Jaya algorithm. Heat Transf Eng. Google Scholar
  36. 36.
    Rao RV, More KC (2017) Optimal design and analysis of mechanical draft cooling tower using improved Jaya algorithm. Int J Refrig 82:312–324Google Scholar
  37. 37.
    Nayak RK, Mishra D, Rath AK (2015) A Naïve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices. Appl Soft Comput 35:670–680Google Scholar
  38. 38.
    Rout Minakhi, Majhi Babita, Majhi Ritanjali, Panda Ganapati (2014) Forecasting of currency exchange rates using an adaptive ARMA model with differential evolution based training. J King Saud Univ Comput Inf Sci 26(1):7–18Google Scholar
  39. 39.
    Li C, Nguyen TT, Yang M, Yang S, Zeng S (2015) Multi-population methods in unconstrained continuous dynamic environments: the challenges. Inf Sci 296:95–118Google Scholar
  40. 40.
    Rao RV, More KC (2017) Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm. Energy Convers Manag 140:24–35Google Scholar
  41. 41.
    Tissera MD, McDonnell MD (2016) Deep extreme learning machines: supervised autoencoding architecture for classification. Neurocomputing 174:42–49Google Scholar
  42. 42.
    Majhi B, Rout M, Baghel V (2014) On the development and performance evaluation of a multiobjective GA-based RBF adaptive model for the prediction of stock indices. J King Saud Univ Comput Inf Sci 26(3):319–331Google Scholar
  43. 43.
    Ferreira TA, Vasconcelos GC, Adeodato PJ (2008) A new intelligent system methodology for time series forecasting with artificial neural networks. Neural Process Lett 28(2):113–129Google Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Smruti Rekha Das
    • 1
    Email author
  • Debahuti Mishra
    • 1
  • Minakhi Rout
    • 2
  1. 1.Department of Computer Science and EngineeringSiksha ‘O’ Anusandhan Deemed UniversityBhubaneswarIndia
  2. 2.School of Computer EngineeringKIIT Deemed UniversityBhubaneswarIndia

Personalised recommendations