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Fusion of multiple indicators with ensemble incremental learning techniques for stock price forecasting

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Abstract

Predicting stock market index is very challenging as financial time series shows highly non-linear and non-stationary patterns. In this paper, an ensemble incremental learning model is presented for stock price forecasting, which is composed of two decomposition methods: discrete wavelet transform (DWT) and empirical mode decomposition (EMD), as well as two learning models: random vector functional link network (RVFL) and support vector regression (SVR). Firstly, DWT and EMD are sequentially combined to decompose the historical stock price time series, followed by RVFL models to analyze the obtained sub-signals and generate predictions. Moreover, ten stock market indicators are used to improve the performance of the ensemble model. Last but not least, incremental learning with RVFL also benefits the performance significantly. To evaluate the proposed DWT-EMD-RVFL-SVR model, stock price forecasting for five power related companies are conducted to compare with seven benchmark methods and two recently published works.

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References

  1. Qiu X, Ren Y, Suganthan PN, Amaratumga GAJ (2017) Empirical mode decomposition based ensemble deep learning for load demand time series forecasting. Appl Soft Comput 54:246–255

    Article  Google Scholar 

  2. Barak S, Arjmand A, Ortobelli S (2017) Fusion of multiple diverse predictors in stock market. Inf Fusion 36:90–102

    Article  Google Scholar 

  3. He K, Zha R, Wu J, Lai KK (2016) Multivariate EMD-based modeling and forecasting of crude oil price. Sustainability 8(4):387

    Article  Google Scholar 

  4. Ren Y, Suganthan PN, Srikanth N, Amaratunga G (2016) Random vector functional link network for short-term electricity load demand forecasting. Inf Sci 367–368:1078–1093

    Article  Google Scholar 

  5. Holt CC (2004) Forecasting seasonals and trends by exponentially weighted moving averages. Int J Forecast 20:5–10

    Article  Google Scholar 

  6. Papalexopoulos AD, Hesterberg TC (1990) A regression-based approach to short-term system load forecasting. IEEE Trans Power Syst 5:1535–1547

    Article  Google Scholar 

  7. Box GEP, Jenkins G (1990) Time series analysis, forecasting and control. Holden-Day Inc, San Francisco. ISBN 0816211043

    MATH  Google Scholar 

  8. Engle RF (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50:987–1008

    Article  MathSciNet  MATH  Google Scholar 

  9. Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econ 31:307–327

    Article  MathSciNet  MATH  Google Scholar 

  10. Kazem A, Sharifi E, Hussain FK, Saberi M, Hussain OK (2013) Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Appl Soft Comput 13:947–958

    Article  Google Scholar 

  11. Kohavi R, Provost F (1998) Glossary of terms. Mach Learn 30:271–274

    Article  Google Scholar 

  12. Ravi V, Pradeepkumar D, Deb K (2017) Financial time series prediction using hybrids of chaos theory, multi-layer perceptron and multi-objective evolutionary algorithms. Swarm Evolut Comput 36:136–149

    Article  Google Scholar 

  13. Pradeepkumar D, Ravi V (2017) Forecasting financial time series volatility using particle swarm optimization trained quantile regression neural network. Appl Soft Comput 58:35–52

    Article  Google Scholar 

  14. Darbellay GA, Slama M (2000) Forecasting the short-term demand for electricity: Do neural networks stand a better chance? Int J Forecast 16:71–83

    Article  Google Scholar 

  15. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  16. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  17. Kim K, Han I (2008) Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Syst Appl 19:125–132

    Article  Google Scholar 

  18. Yu L, Wang S, Lai KK (2009) A neural-network-based nonlinear metamodeling approach to financial time series forecasting. Appl Soft Comput 9:563–574

    Article  Google Scholar 

  19. Vapnik V (1995) The nature of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  20. Yeh C-Y, Huang C-W, Lee S-J (2010) A multiple-kernel support vector regression approach for stock market price forecasting. Expert Syst Appl 38:2177–2186

    Article  Google Scholar 

  21. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems. Curran Associates, New York, pp 1097–1105

    Google Scholar 

  22. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    Article  MathSciNet  MATH  Google Scholar 

  23. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  24. Ding X, Zhang Y, Liu T, Duan J (2015) Deep learning for event-driven stock prediction. In: Proceedings of the twenty-fourth international joint conference on artificial intelligence (IJCAI 2015), AAAI Press, pp 2327–2333

  25. Akita R, Yoshihara A, Matsubara T, Uehara K (2016) Deep learning for stock prediction using numerical and textual information. In: Proceedings of IEEE/ACIS 15th international conference on computer and information science (ICIS2016), Okayama, Japan

  26. Pao Y-H, Phillips SM, Sobajic DJ (1992) Neural-net computing and the intelligent control of systems. Int J Control 56:263–289

    Article  MathSciNet  MATH  Google Scholar 

  27. Pao Y-H, Phillips SM, Sobajic DJ (1994) Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6:163–180

    Article  Google Scholar 

  28. Schmidt WF, Kraaijveld MA, Duin RPW (1992) Feedforward neural networks with random weights. In: Proceedings of the IAPR international conference on pattern recognition conference B: pattern recognition methodology and systems, pp 1–4

  29. Zhang L, Suganthan PN (2016) A comprehensive evaluation of random vector functional link networks. Inf Sci 367–368:1094–1105

    Article  Google Scholar 

  30. Dietterich TG (2000) Ensemble methods in machine learning. In: Multiple classifier systems. Lecture notes in computer science, vol 1857. Springer, Berlin, Heidelberg

    Google Scholar 

  31. Ren Y, Zhang L, Suganthan PN (2016) Ensemble classification and regression-recent developments, applications and future directions (review article). IEEE Comput Intell Mag 11(1):41–53

    Article  Google Scholar 

  32. Breiman L (1996) Stacked regressions. Mach Learn 24:49–64

    MATH  Google Scholar 

  33. Qiu X, Zhang L, Ren Y, Suganthan PN, Amaratunga G (2014) Ensemble deep learning for regression and time series forecasting. In: Proceedings of IEEE symposium on computational intelligence in ensemble learning (CIEL), Orlando, USA

  34. Cormen TH, Leiserson CE, Rivest RL, Stein C (2000) Introduction to algorithms. MIT Press, Cambridge

    MATH  Google Scholar 

  35. Guan C, Luh PB, Michel LD, Wang Y, Friedland PB (2013) Very short-term load forecasting: wavelet neural networks with data pre-filtering. IEEE Trans Power Syst 28(1):30–41

    Article  Google Scholar 

  36. Hooshmand R-A, Amooshahi H, Parastegari M (2013) A hybrid intelligent algorithms based short-term load forecasting approach. Int J Electr Power Energy Syst 45:313–324

    Article  Google Scholar 

  37. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH (1998) The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. R Soc Lond A 454:903–995

    Article  MathSciNet  MATH  Google Scholar 

  38. Haykin S (1999) Neural networks: a comprehensive foundation, International edn. Prentice Hall, Upper Saddle River

    MATH  Google Scholar 

  39. Liu H, Chen C, Tian H, Li Y (2012) A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks. Renew Energy 48:545–556

    Article  Google Scholar 

  40. Qiu X, Suganthan PN, Amaratunga GAJ (2016) Electricity load demand time series forecasting with empirical mode decomposition based random vector functional link network. In: Proceedings of IEEE conference on systems, man and cybernetics (SMC2016), Budapest, Hungary

  41. Ye L, Liu P (2011) Combined model based on EMD-SVM for short-term wind power prediction. In: Proceedings of Chinese society for electrical engineering (CSEE), vol 31, pp 102–108

  42. Ren Y, Qiu X, Suganthan PN, Srikanth N, Amaratunga G (2015) Detecting wind power ramp with random vector functional link (RVFL) network. In: Proceedings of IEEE symposium series on computational intelligence (CIEL2015), Cape Town, South Africa

  43. Grossmann A, Morlet J (1984) Decomposition of hardy functions into square integrable wavelets of constant shape. SIAM J Math Anal 15:723–736

    Article  MathSciNet  MATH  Google Scholar 

  44. Kiplangat DC, Asokan K, Kumar KS (2016) Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition. Renew Energy 93:38–44

    Article  Google Scholar 

  45. Percival D, Walden A (2006) Wavelet methods for time series analysis, Cambridge series in statistical and probabilistic mathematics. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  46. Percival DB, Wang M, Overland JE (2004) An introduction to wavelet analysis with applications to vegetation time series. Community Ecol 5(1):19–30

    Article  Google Scholar 

  47. Chen C, Wan J (1999) A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction. IEEE Trans Syst Man Cybern Part B (Cybern) 29(1):62–72

    Article  Google Scholar 

  48. Kara Y, Boyacioglu MA (2011) Ömer Kaan Baykan, Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul Stock Exchange. Expert Syst Appl 38:5311–5319

    Article  Google Scholar 

  49. Chen Y, Feng MQ (2003) A technique to improve the empirical mode decomposition in the hilbert-huang transform. Earthq Eng Eng Vib 2:796–808

    Google Scholar 

  50. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1:1–41

    Article  Google Scholar 

  51. Yahoo finance (2017). http://www.finance.yahoo.com/. Accessed Sept 2017

  52. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701

    Article  MATH  Google Scholar 

  53. Nemenyi P (1963) Distribution-free multiple comparisons. Princeton University, Princeton

    Google Scholar 

  54. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

  55. Patel J, Shah S, Thakkar P, Kotecha K (2015) Predicting stock market index using fusion of machine learning techniques. Expert Syst Appl 42:2162–2172

    Article  Google Scholar 

  56. Hafezi R, Shahrabi J, Hadavandi E (2015) A bat-neural network multi-agent system (BNNMAS) for stock price prediction: case study of DAX stock price. Appl Soft Comput 29:196–210

    Article  Google Scholar 

Download references

Acknowledgements

This project is funded by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    Xueheng Qiu & Ponnuthurai Nagaratnam Suganthan

  2. Centre for Advanced Photonics and Electronics, Electrical Engineering Division, Engineering Department, University of Cambridge, Cambridge, CB3 0FA, UK

    Gehan A. J. Amaratunga

Authors
  1. Xueheng Qiu
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  2. Ponnuthurai Nagaratnam Suganthan
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  3. Gehan A. J. Amaratunga
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Correspondence to Ponnuthurai Nagaratnam Suganthan.

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Qiu, X., Suganthan, P.N. & Amaratunga, G.A.J. Fusion of multiple indicators with ensemble incremental learning techniques for stock price forecasting. J BANK FINANC TECHNOL 3, 33–42 (2019). https://doi.org/10.1007/s42786-018-00006-2

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