IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v513y2019icp339-370.html
   My bibliography  Save this article

An optimized feature reduction based currency forecasting model exploring the online sequential extreme learning machine and krill herd strategies

Author

Listed:
  • Das, Smruti Rekha
  • Kuhoo,
  • Mishra, Debahuti
  • Rout, Minakhi

Abstract

For the prediction of exchange rate, this paper proposes a hybrid learning frame work model which is a joint estimation of On-Line Sequential Extreme Learning Machine (OS-ELM) along with optimized feature reduction using Krill Herd (KH). The proposed learning scheme is compared with Extreme Learning Machine (ELM) and Recurrent Back Propagation Neural Network (RBPNN), considering three factors such as; without feature reduction, with statistical based feature reduction using Principal Component Analysis (PCA) and with optimized feature reduction techniques such as KH, Bacteria Foraging Optimization (BFO) and Particle Swarm Optimization (PSO). The models are applied over USD/INR, USD/EURO, YEN/INR and SGD/INR, constructed using technical indicators and statistical measures considering 3, 5, 7, 12 and 15 as window sizes. The results of comparisons of different performance measures in testing phase and MSE in training process demonstrate that the proposed OSELM-KH exchange rate prediction model is potentiality superior compared to others.

Suggested Citation

  • Das, Smruti Rekha & Kuhoo, & Mishra, Debahuti & Rout, Minakhi, 2019. "An optimized feature reduction based currency forecasting model exploring the online sequential extreme learning machine and krill herd strategies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 339-370.
    • Handle: RePEc:eee:phsmap:v:513:y:2019:i:c:p:339-370
    DOI: 10.1016/j.physa.2018.09.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118311476
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.09.021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rajagopal, 2015. "Market Trend Analysis," Palgrave Macmillan Books, in: The Butterfly Effect in Competitive Markets, chapter 4, pages 95-118, Palgrave Macmillan.
    2. Sermpinis, Georgios & Theofilatos, Konstantinos & Karathanasopoulos, Andreas & Georgopoulos, Efstratios F. & Dunis, Christian, 2013. "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization," European Journal of Operational Research, Elsevier, vol. 225(3), pages 528-540.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. David Alaminos & M. Belén Salas & Manuel Á. Fernández-Gámez, 2023. "Quantum Monte Carlo simulations for estimating FOREX markets: a speculative attacks experience," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-21, December.
    2. Zhijian Wang & Likang Zheng & Junyuan Wang & Wenhua Du, 2019. "Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine," Complexity, Hindawi, vol. 2019, pages 1-19, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jahangoshai Rezaee, Mustafa & Jozmaleki, Mehrdad & Valipour, Mahsa, 2018. "Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 78-93.
    2. Sun, Shaolong & Wang, Shouyang & Wei, Yunjie, 2019. "A new multiscale decomposition ensemble approach for forecasting exchange rates," Economic Modelling, Elsevier, vol. 81(C), pages 49-58.
    3. Packey, Daniel J. & Kingsnorth, Dudley, 2016. "The impact of unregulated ionic clay rare earth mining in China," Resources Policy, Elsevier, vol. 48(C), pages 112-116.
    4. Mehdi Pedram & Maryam Ebrahimi, 2015. "The Effects of Economic Variables on Exchange Rate, Modeling and Forecasting: Case of Iran," Business and Management Horizons, Macrothink Institute, vol. 3(1), pages 13-23, June.
    5. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    6. Diego Lopez-Bernal & David Balderas & Pedro Ponce & Arturo Molina, 2021. "Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems," Future Internet, MDPI, vol. 13(8), pages 1-14, July.
    7. Fischer, Thomas & Krauss, Christopher, 2017. "Deep learning with long short-term memory networks for financial market predictions," FAU Discussion Papers in Economics 11/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    8. Paloviita, Maritta & Haavio, Markus & Jalasjoki, Pirkka & Kilponen, Juha & Vänni, Ilona, 2020. "Reading between the lines : Using text analysis to estimate the loss function of the ECB," Research Discussion Papers 12/2020, Bank of Finland.
    9. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    10. Hyejung Chung & Kyung-shik Shin, 2018. "Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction," Sustainability, MDPI, vol. 10(10), pages 1-18, October.
    11. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2018. "Forecasting exchange rate using Variational Mode Decomposition and entropy theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 15-25.
    12. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
    13. Julien Chevallier & Bangzhu Zhu & Lyuyuan Zhang, 2021. "Forecasting Inflection Points: Hybrid Methods with Multiscale Machine Learning Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 537-575, February.
    14. Jani KINNUNEN & Armenia ANDRONICEANU & Irina GEORGESCU, 2019. "Digitalization Of Eu Countries: A Clusterwise Analysis," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 13(1), pages 1-12, November.
    15. Kwon, He-Boong, 2017. "Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 159-170.
    16. Elona Marku & Manuel Castriotta & Michela Loi & Maria Chiara Di Guardo, 2021. "General Purpose Technology: The Blockchain Domain," International Journal of Business and Management, Canadian Center of Science and Education, vol. 15(11), pages 192-192, July.
    17. Asit Kumar Das & Debahuti Mishra & Kaberi Das & Pradeep Kumar Mallick & Sachin Kumar & Mikhail Zymbler & Hesham El-Sayed, 2022. "Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine," Mathematics, MDPI, vol. 10(7), pages 1-33, March.
    18. Chen, Yan & Wang, Xuancheng, 2015. "A hybrid stock trading system using genetic network programming and mean conditional value-at-risk," European Journal of Operational Research, Elsevier, vol. 240(3), pages 861-871.
    19. Umair Khan & Farhan Aadil & Mustansar Ali Ghazanfar & Salabat Khan & Noura Metawa & Khan Muhammad & Irfan Mehmood & Yunyoung Nam, 2018. "A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets," Sustainability, MDPI, vol. 10(10), pages 1-20, October.
    20. Miriyala, Srinivas Soumitri & Subramanian, Venkat & Mitra, Kishalay, 2018. "TRANSFORM-ANN for online optimization of complex industrial processes: Casting process as case study," European Journal of Operational Research, Elsevier, vol. 264(1), pages 294-309.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:513:y:2019:i:c:p:339-370. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.