An Integrated Approach Incorporating Nonlinear Dynamics and Machine Learning for Predictive Analytics and Delving Causal Interaction

  • Indranil Ghosh
  • Manas K. Sanyal
  • R. K. Jana
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)


Development of predictive modeling framework for observational data, exhibiting nonlinear and random characteristics, is a challenging task. In this study, a neoteric framework comprising tools of nonlinear dynamics and machine learning has been presented to carry out predictive modeling and assessing causal interrelationships of financial markets. Fractal analysis and recurrent quantification analysis are two components of nonlinear dynamics that have been applied to comprehend the evolutional dynamics of the markets in order to distinguish between a perfect random series and a biased one. Subsequently, three machine learning algorithms, namely random forest, boosting and group method of data handling, have been adopted for forecasting the future figures. Apart from proper identification of nature of the pattern and performing predictive modeling, effort has been made to discover long-rung interactions or co-movements among the said markets through Bayesian belief network as well. We have considered daily data of price of crude oil and natural gas, NIFTY energy index, and US dollar-Rupee rate for empirical analyses. Results justify the usage of presented research framework in effective forecasting and modeling causal influence.


  1. 1.
    Tao, H.: A wavelet neural network model for forecasting exchange rate integrated with genetic algorithm. IJCSNS Int. J. Comput. Sci. Netw. Sec. 6, 60–63 (2006)Google Scholar
  2. 2.
    Rather, A.M., Agarwal, A., Sastry, V.N.: Recurrent neural network and a hybrid model for prediction of stock returns. Expert Syst. Appl. 42, 3234–3241 (2015)CrossRefGoogle Scholar
  3. 3.
    Ramasamy, P., Chandel, S.S., Yadav, A.K.: Wind speed prediction in the mountainous region of India using an artificial neural network model. Renew. Energy 80, 338–347 (2015)CrossRefGoogle Scholar
  4. 4.
    Yin, K., Zhang, H., Zhang, W., Wei, Q.: Fractal analysis of gold market in China. Rom. J. Econ. Forecast. 16, 144–163 (2013)Google Scholar
  5. 5.
    Hurst, H.E.: Long-term storage capacity of reservoirs. Trans. Am. Soc. Civ. Eng. 116, 770–808 (1951)Google Scholar
  6. 6.
    Mandelbrot, B., Wallis, J.: Noah, Joseph and operational hydrology. Water Resour. Res. 4, 909–918 (1968)CrossRefGoogle Scholar
  7. 7.
    Eckmann, J.P., Kamphorst, S.O., Ruelle, D.: Recurrence plot of dynamical system. Europhys. Lett. 4, 4973–4977 (1987)CrossRefGoogle Scholar
  8. 8.
    Webber, C.L., Zbilut, J.P.: Dynamical assessment of physiological systems and states using recurrence plot strategies. J. Appl. Physiol. 76, 965–973 (1994)CrossRefGoogle Scholar
  9. 9.
    Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)CrossRefGoogle Scholar
  10. 10.
    Xie, Y., Li, X., Ngai, E.W.T., Ying, W.: Customer churn prediction using improved balanced random forests. Expert Syst. Appl. 36, 5445–5449 (2009)CrossRefGoogle Scholar
  11. 11.
    Lariviere, B., Van den Poel, D.: Predicting customer retention and profitability by random forests and regression forests techniques. Expert Syst. Appl. 29, 472–484 (2005)CrossRefGoogle Scholar
  12. 12.
    Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37, 297–336 (1999)CrossRefGoogle Scholar
  13. 13.
    Collins, M., Schapire, R.E., Singer, Y.: Logistic Regression, AdaBoost and Bregman distances. Mach. Learn. 48, 253–285 (2002)CrossRefGoogle Scholar
  14. 14.
    Cortes, E.A., Martinez, M.G., Rubio, N.G.: Multiclass corporate failure prediction by Adaboost. M1. Int. Adv. Econ. Res. 13, 301–312 (2007)CrossRefGoogle Scholar
  15. 15.
    Leshem, G., Ritov, Y.: Traffic flow prediction using Adaboost Algorithm with random forests as a weak learner. Int. J. Math. Comput. Phys. Electr. Comput. Eng. 1, 1–6 (2007)Google Scholar
  16. 16.
    Ivakhnenko, A.G.: The group method of data handling—a rival of the method of stochastic approximation. Soviet Autom. Control 13, 43–55 (1968)Google Scholar
  17. 17.
    Zhang, M., He, C., Liatsis, P.: A D-GMDH model for time series forecasting. Expert Syst. Appl. 39, 5711–5716 (2012)CrossRefGoogle Scholar
  18. 18.
    Najafzadeh, M., Barani, G.H., Azamathulla, H.M.: GMDH to predict scour depth around a pier in cohesive soils. Appl. Ocean Res. 40, 35–41 (2013)CrossRefGoogle Scholar
  19. 19.
    Scutari, M.: Learning Bayesian Networks with the bnlearn R Package. J. Stat. Softw. 35, 1–22 (2010)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Operations ManagementCalcutta Business SchoolKolkataIndia
  2. 2.Department of Business AdministrationUniversity of KalyaniKalyaniIndia
  3. 3.Indian Institute of Management RaipurRaipurIndia

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