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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. Rajagopal, 2015. "Market Trend Analysis," Palgrave Macmillan Books, in: The Butterfly Effect in Competitive Markets, chapter 4, pages 95-118, Palgrave Macmillan.
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    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. Ma, Changxi & Zhao, Mingxi & Huang, Xiaoting & Zhao, Yongpeng, 2024. "Optimized deep extreme learning machine for traffic prediction and autonomous vehicle lane change decision-making," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    3. 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.

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