[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

A novel hybrid model using teaching–learning-based optimization and a support vector machine for commodity futures index forecasting

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

The analysis and prediction of financial time-series data are difficult, and are the most complicated tasks concerned with improving investment decisions. In this study, we forecasted a financial derivatives instrument (the commodity futures contract index) using techniques based on recently developed machine learning techniques. These methods have been shown to perform remarkably well in other applications. In particular, we developed a hybrid method that combines a support vector machine (SVM) with teaching–learning-based optimization (TLBO). The proposed SVM–TLBO model avoids user-specified control parameters, which are required when using other optimization methods. We assessed the viability and efficiency of this hybrid model by forecasting the daily closing prices of the COMDEX commodity futures index, traded in the Multi Commodity Exchange of India Limited. Our experimental results show that the proposed model is effective and performs better than the particle swarm optimization (PSO) + SVM hybrid and standard SVM models. For example, the proposed model improved the MAE by 65.87 % (1-day-ahead forecast), 55.83 % (3-days-ahead forecast), and 67.03 % (5-days-ahead forecast), when compared with standard SVM regression.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Bose S (2008) Commodity futures market in India: a study of trends in the national multi-commodity indices. ICRA Bull Money Finance 3(3):125–158

    Google Scholar 

  2. Cao LJ, Tay FEH (2003) Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans Neural Networks 14(6):1506–1518

    Article  Google Scholar 

  3. Chauhan P, Pant M, Deep K (2015) Parameter optimization of multi-pass turning using chaotic PSO. Int J Mach Learn Cybern 6:319–337. doi:10.1007/s13042-013-0221-1

    Article  Google Scholar 

  4. Cherkassy V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17(1):113–126

    Article  MATH  Google Scholar 

  5. Chih-Chung C, Chin-Jen (2001) LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1–27:27. (Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvmLIBSVM)

  6. Chih-Ming H (2013) A hybrid procedure with feature selection for resolving stock/futures price forecasting problems. Neural Comput Appl 2013:651–671. doi:10.1007/s00521-011-07214

    Google Scholar 

  7. Gestel TV, Suykens JAK, Baestaens D-E, Lambrechts A, Lanckriet G, Vandaele B, Moor BD, Vandewalle J (2001) Financial time-series prediction using least squares support vector machines within the evidence framework. IEEE Trans Neural Netw 12(4):809–821

    Article  Google Scholar 

  8. Huang CL, Tsai CY (2009) A hybrid SOFM-SVR with a filter based feature selection for stock market forecasting. Expert Syst Appl 36(2):1529–1539. doi:10.1016/j.eswa.2007.11.062

    Article  MathSciNet  Google Scholar 

  9. Ince H, Trafalis TB (2008) Short term forecasting with support vector machines and application to stock price prediction. Int J Gen Syst 37(6):677–687. doi:10.1080/03081070601068595

    Article  MathSciNet  MATH  Google Scholar 

  10. Ito K, Nakano R (2005) Optimizing support vector regression hyper-parameters based on cross-validation. Proc Int Jt Conf Neural Netw 3:871–876

    Google Scholar 

  11. Jain SK, Patnaik A, Sinha SN (2013) Design of custom-made stacked patch antennas: a machine learning approach. Int J Mach Learn Cybern 4:189–194. doi:10.1007/s13042-012-0084-x

    Article  Google Scholar 

  12. Jiang M, Jiang S, Zhu L, Wang Y, Huang W, Zhang H (2013) Study on parameter optimization for support vector regression in solving the inverse ECG problem. Comput Math Methods Med. doi:10.1155/2013/158056 Article ID 158059

    MathSciNet  MATH  Google Scholar 

  13. Keerthi SS (2002) Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms. IEEE Trans Neural Netw 13(5):1225–1229

    Article  Google Scholar 

  14. Kim K (2003) Financial time series forecasting using support vector machines. Neurocomputing 55:307–319

    Article  Google Scholar 

  15. Kim KJ, Han I (2000) Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Syst Appl 19(2):125–132

    Article  Google Scholar 

  16. Kim KJ, Lee WB (2004) Stock market prediction using artificial neural networks with optimal feature transformation. Neural Comput Appl 13(3):255–260. doi:10.1007/s00521-004-0428-x

    Article  Google Scholar 

  17. Lai RK, Fan CY, Huang WH, Chang PC (2009) Evolving and clustering fuzzy decision tree for financial time series data forecasting. Expert Syst Appl 36(2):3761–3773. doi:10.1016/j.eswa.2008.02.025

    Article  Google Scholar 

  18. Leung MT, Daouk H, Chen AS (2000) Forecasting stock indices: a comparison of classification and level estimation models. Int J Forecast 16:173–190

    Article  Google Scholar 

  19. Liang X, Zhang HS, Mao JG, Chen Y (2009) Improving option price forecasts with neural networks and support vector regressions. Neurocomputing 72(13–15):3055–3065. doi:10.1016/j.neucom.2009.03.015

    Article  Google Scholar 

  20. Lin H-T, Lin C-J (2003) A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Technical report, University of National Taiwan, Department of Computer Science and Information Engineering. March, pp 1–32

  21. Lin SW, Ying K-C, Chen S-C, Lee Z-J (2008) Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst Appl 35:1817–1824

    Article  Google Scholar 

  22. Liu S, Tian L, Huang Y (2014) A comparative study on prediction of throughput in coal ports among three models. Int J Mach Learn Cybern 5:125–133. doi:10.1007/s13042-013-0201-5

    Article  Google Scholar 

  23. Liu Z, Wu Q, Zhang Y, Philip Chen CL (2011) Adaptive least squares support vector machines filter for hand tremor canceling in microsurgery. Int J Mach Learn Cybern 2:37–47. doi:10.1007/s13042-011-0012-5

    Article  Google Scholar 

  24. Musa AB (2013) Comparative study on classification performance between support vector machine and logistic regression. Int J Mach Learn Cybern 4:13–24. doi:10.1007/s13042-012-0068-x

    Article  Google Scholar 

  25. Nicholas IS, Ravi S (2009) Time series prediction using support vector machines: a survey. IEEE Comput Intel Mag 4(2):24–38. doi:10.1109/MCI.2009.932254

    Article  Google Scholar 

  26. Pawar PV, Rao RV (2013) Parameter optimization of machining using teaching-learning-based optimization algorithm. Int J Adv Manuf Technol 67:995–1006

    Article  Google Scholar 

  27. Rao RV, Kalyankar VD (2012) Parameter optimization of machining processes using a new optimization algorithm. Mater Manuf Process 27(9):978–985

    Article  Google Scholar 

  28. Rao RV, Kalyankar VD, Waghmare G (2014) Parameters optimization of selected casting processes using teaching-learning-based optimization algorithm. Appl Math Model 38:5592–5608. doi:10.1016/j.apm.2014.04.036

    Article  Google Scholar 

  29. Rao RV, Patel V (2014) A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems. Int J Ind Eng Comput 5:1–22. doi:10.5267/j.ijiec.2013.09.007

    Google Scholar 

  30. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315

    Article  Google Scholar 

  31. Rao RV, Savsani VJ, Vakharia DP (2012) Teaching-learning-based optimization: a optimization method for continuous non-linear large scale problems. Inf Sci 183:1–15

    Article  MathSciNet  Google Scholar 

  32. Rao RV, Waghmare GG (2014) A comparative study of a teaching-learning-based optimization algorithm on multi-objective unconstrained and constrained functions. J King Saud Univ Comput Inf Sci 26(3):332–346. doi:10.1016/j.jksuci.2013.12.004

    Google Scholar 

  33. Rao RV, Waghmare GG (2015) Multi-objective design optimization of a plate-fin heat sink using a teaching-learning-based optimization algorithm. Appl Therm Eng 76:521–529. doi:10.1016/j.applthermaleng.2014.11.052

    Article  Google Scholar 

  34. Refenes AP, Zapranis AD, Francis G (1995) Modeling stock returns in the framework of APT: a comparative study with regression models. In: Refenes AP (ed) Neural Networks in the Capital Markets. Wiley, Chichester, pp 101–125

  35. Satapathy SC, Naik A, Parvathi A (2013) A teaching learning based optimization based on orthogonal design for solving global optimization problems. SpringerPlus 2013(2):130. doi:10.1186/2193-1801-2-130

    Article  Google Scholar 

  36. Shazly MRE, Shazly HEE (1999) Forecasting currency prices using genetically evolved neural network architecture. Int Rev Financ Anal 8(1):67–82

    Article  Google Scholar 

  37. Steiner M, Wittkemper HG (1995) Neural networks as an alternative stock market model. In: Refenes AP (ed) Neural Networks in the Capital Markets. Wiley, Chichester, pp 135–147

  38. Tay FEH, Cao L (2002) Modified support vector machines in financial time series forecasting. Neurocomputing 48:847–861

    Article  MATH  Google Scholar 

  39. Tsang PM, Kwok P, Choy SO, Kwan R, Ng SC, Mak J, Tsang J, Koong K, Wong TL (2007) Design and implementation of NN5 for Hong Kong stock price forecasting. Eng Appl Artif Intell 20(4):453–461. doi:10.1016/j.engappai.2006.10.002

    Article  Google Scholar 

  40. Tsibouris G, Zeidenberg M (1995) Testing the efficient markets hypothesis with gradient descent algorithms. Neural Networks in the Capital Markets, pp 127–136

  41. Vapnik V (1995) The Nature of Statistical Learning Theory. Springer, New York (ISBN 0-387-94559-8)

    Book  MATH  Google Scholar 

  42. Wittkemper HG, Steiner M (1996) Using neural networks to forecast the systematic risk of stocks. Eur J Oper Res 90:577–588

    Article  MATH  Google Scholar 

  43. Wun-Hua C, Jen-Ying S, Soushan W (2006) Comparison of support vector machines and back propagation neural networks in forecasting the six major Asian stock markets. Int J Electron Finance 1(1):49–67

    Article  Google Scholar 

  44. Yang Y, Wang G, Yang Y (2014) Parameters optimization of polygonal fuzzy neural networks based on GA-BP hybrid algorithm. Int J Mach Learn Cybern 5:815–822. doi:10.1007/s13042-013-0224-y

    Article  Google Scholar 

  45. Zou F, Wang L, Hei X, Chen D, Yang D (2014) Teaching-learning-based optimization with dynamic group strategy for global optimization. Inf Sci 273:112–131. doi:10.1016/j.ins.2014.03.038

    Article  Google Scholar 

Download references

Acknowledgments

We would like to express our gratitude to the National Institute of Science and Technology (NIST), for the facilities and resources provided at the Data Science Laboratory at NIST for the development of this study. The authors would also like to thank the editor and the anonymous reviewers for their innovative suggestions that improved the quality of this manuscript.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Science and Technology, Palur Hills, Berhampur, Odisha, 761008, India

    Shom Prasad Das

  2. Institute of Mathematics and Applications, Bhubaneswar, Odisha, 751003, India

    Sudarsan Padhy

Authors
  1. Shom Prasad Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sudarsan Padhy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shom Prasad Das.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Das, S.P., Padhy, S. A novel hybrid model using teaching–learning-based optimization and a support vector machine for commodity futures index forecasting. Int. J. Mach. Learn. & Cyber. 9, 97–111 (2018). https://doi.org/10.1007/s13042-015-0359-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-015-0359-0

Keywords

Search

Navigation