Abstract
Financial forecasting is a widely applied area, making use of statistical prediction using ARMA, ARIMA, ARCH and GARCH models on stock prices. Such data have unpredictable trends and non-stationary property which makes even the best long term predictions grossly inaccurate. The problem is countered by keeping the prediction shorter. These methods are based on time series models like auto regressions and moving averages, which require computationally costly recurring parameter estimations. When the data size becomes considerable, we need Big Data tools and techniques, which do not work well with time series computations. In this paper we discuss such a finance domain problem on the Indian National Stock Exchange (NSE) data for a period of one year. Our main objective is to device a light weight prediction for the bulk of companies with fair accuracy, useful enough for algorithmic trading. We present a minimal discussion on these classical models followed by our Spark RDD based implementation of the proposed fast forecast model and some results we have obtained.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Merh, N., Saxena, V.P., Pardasani, K.R.: Next day stock market forecasting: an application of ANN and ARIMA. IUP J. Appl. Finan. 17(1), 70–85 (2011)
Pai, P.F., Lin, C.S.: A hybrid ARIMA and support vector machines model in stock price forecasting. Omega 33(6), 497–505 (2005)
Vengertsev, D.: Deep learning architecture for univariate time series forecasting. Technical report, Stanford University (2014)
Sun, G., et al.: A carbon price forecasting model based on variational mode decomposition and spiking neural networks. Energies. 9(1), 54 (2016)
Chrétien, S.,Wei, T., Al-Sarray, B.A.H.: Joint estimation and model order selection for one dimensional ARMA models via convex optimization: a nuclear norm penalization approach. arXiv preprint arXiv:1508.01681 (2015)
Chen, Y., Lai, K.K., Du, J.: Modeling and forecasting Hang Seng Index Volatility with day-of-week effect, spillover effect based on ARIMA and HAR. Eurasian Econ. Rev. 4(2), 113–132 (2014)
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M.J., Shenker, S., Stoica, I.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: 9th USENIX Conference on Networked Systems Design and Implementation (NSDI 2012). USENIX Association, Berkeley (2012)
Engle, R.F.: Autoregressive conditional Heteroscedasticity with estimates of Variance of United Kingdom inflation. Econometrica 50, 987–1007 (1982)
Kita, E., Zuo, Y., Harada, M., Mizuno, T.: Application of Bayesian Network to stock price prediction. Artif. Intell. Res. 1(2), 171–184 (2012)
Sandgren, N., Stoica, P.: On moving average parameter estimation. In: 20th European Signal Processing Conference (EUSIPCO), Bucharest, Romania, pp. 2348–2351 (2012)
Al-Shiab, M.: The predictability of the amman stock exchange using univariate autoregressive integrated moving average (ARIMA) model. J. Econ. Adm. Sci. 22(2), 17–35 (2006)
Nau, R.: Fuqua School of Business, Duke University. http://people.duke.edu/~rnau/Mathematical_structure_of_ARIMA_models–Robert_Nau.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Menon, V.K., Chekravarthi Vasireddy, N., Jami, S.A., Pedamallu, V.T.N., Sureshkumar, V., Soman, K.P. (2016). Bulk Price Forecasting Using Spark over NSE Data Set. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2016. Lecture Notes in Computer Science(), vol 9714. Springer, Cham. https://doi.org/10.1007/978-3-319-40973-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-40973-3_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40972-6
Online ISBN: 978-3-319-40973-3
eBook Packages: Computer ScienceComputer Science (R0)