Abstract
Significance of this research is to accomplish tentative study on highs and lows of particular S&P BSE stock prices using proposed intelligent models, multivariate adaptive regression spline (MARS) and M5 prime regression tree-(M5’). Anticipated models work to predict as there exists vitality for price instability. Daily highs and lows of the stock price data have been considered as the data set. This article discusses about computational ability of the MARS and M5’ regressions during the time period and also how better accuracy can be attained. M5’ constructs in two phases: growing and pruning which smoothen regression tree at nodes. MARS builds complex configuration of correlation among multiple responses. This can be helpful for investors to predict significant statistics for trading stocks listed on BSE.
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References
Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific Publishing, Singapore (2002)
Jekabsons, G.: M5 prime lab: M5’ regression tree, model tree, and tree ensemble toolbox for Matlab/Octave (2016). Available at http://www.cs.rtu.lv/jekabsons/
Ekici, S.: Classification of power system disturbances using support vector machines. Expert Syst. Appl. 36(6), 9859–9868 (2009)
Samadi, M, Jabbari, E, Azamathulla, H. Md.: Assessment of M5 model tree and classification and regression trees for prediction of scour depth below free overfall spillways. Neural Comput. Appl. 24, 357–366 (2014)
Quinlan J.R.: Learning with continuous classes. In: Proceedings of 5th Australian Joint Conference on Artificial Intelligence, pp. 343–348. World Scientific, Singapore (1992)
Patel, R., Hiral, M., Parikh, S.: Experimental study on stock market to analyse the impact of the latest demonetization in India. Artif. Intell. Syst. Mach. Learn. 9(6), 106–110 (2017)
Talebi, A., Mahjoobi, J., Dastorani, M.T., Moosavi, V.: Estimation of suspended sediment load using regression trees and model trees approaches (Case study: Hyderabad drainage basin in Iran). ISH J. Hydraul. Eng. 23(2), 212–219 (2017)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Zhang, L., Zhou, W., Jiao, L.: Wavelet support vector machine. IEEE Trans. Syst. Man Cybernet. Part B Cybernet. 34 (2004)
Wang, Y., Witten, I.H.: Induction of model trees for predicting continuous classes. In: Proceedings of the 9th European Conference on Machine Learning Poster Papers, pp. 128–137. Prague (1997)
Witten, I.H., Frank, E.: Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
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Authors are thankful to Guru Gobind Singh, Indraprastha University, for providing financial support and research facilities.
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Bhardwaj, R., Bangia, A. (2020). Assessment of Stock Prices Variation Using Intelligent Machine Learning Techniques for the Prediction of BSE. In: Dutta, D., Mahanty, B. (eds) Numerical Optimization in Engineering and Sciences. Advances in Intelligent Systems and Computing, vol 979. Springer, Singapore. https://doi.org/10.1007/978-981-15-3215-3_15
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DOI: https://doi.org/10.1007/978-981-15-3215-3_15
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