Bulk Price Forecasting Using Spark over NSE Data Set

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• 关键词：Big data ; Spark ; Scala ; Streaming ; RDD ; NSE ; ARMA ; ARIMA ; ARCH ; GARCH ; Financial forecasting ; Econometrics
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9714
• 期：1
• 页码：137-146
• 全文大小：759 KB
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• 作者单位：Vijay Krishna Menon (15)
  Nithin Chekravarthi Vasireddy (16)
  Sai Aswin Jami (16)
  Viswa Teja Naveen Pedamallu (16)
  Varsha Sureshkumar (17)
  K. P. Soman (15)
  
  15. Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore, India
  16. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore, India
  17. Amrita School of Business, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore, India
  
• 丛书名：Data Mining and Big Data
• ISBN：978-3-319-40973-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9714

文摘

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.