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Bulk Price Forecasting using Spark over NSE Data Set

Publication Type : Conference Paper

Publisher : International Conference on Data Mining and Big Data

Source : International Conference on Data Mining and Big Data, Springer, Bali, Indonesia (2016)

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Keywords : ARCH, ARIMA, ARMA, Big data, Econometrics, financial forecasting, GARCH, NSE, RDD, Scala, Spark, Streaming

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking, Electronics Communication and Instrumentation Forum (ECIF)

Department : Center for Computational Engineering and Networking (CEN), Electronics and Communication

Verified : Yes

Year : 2016

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.

Cite this Research Publication : Vijay Krishna Menon, Vasireddy, N. Chakravart, Jam, S. Aswin, Pedamallu, V. Teja Navee, Sureshkumar, V., and Dr. Soman K. P., “Bulk Price Forecasting using Spark over NSE Data Set”, in International Conference on Data Mining and Big Data, Bali, Indonesia, 2016.

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