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Mining the Time Series for Financial Gain
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 92 - Number 3 |
| Year of Publication: 2014 |
| Authors: Rajesh Kumar |
10.5120/15986-4910
|
Rajesh Kumar . Mining the Time Series for Financial Gain. International Journal of Computer Applications. 92, 3 ( April 2014), 1-5. DOI=10.5120/15986-4910
Abstract
Control charts are widely used in finding the process out of control. In the context of financial time series ,change points occurrence is dependent on the sentiments of the traders, hence identification of change point in the financial time series is generally subjective. In this information age, emphasis is on the algorithmic trading where machine has to take trading decisions. In this paper a model is proposed which will take in to the consideration the sentiments of traders, hence volume weighted moving average of ten days is used in identification of sell or purchase signal. Results of the model has been taken in the consideration of worst case, only the closing prices of the month is recorded and trading decision is taken on the restricted data.
References
- Keogh,E & Kasetty, July,200,. On the need for time series data mining benchmarks: A survey and empirical demonstration, In the proceedings of the 8th ACM SIGKDD international conference on knowledge discovery and data mining, Alberta, Canada, pp 102-111.
- Henrik Andre Jonsson, 2002,Dissertation no 757, Indexing strategies for time series data, Department of computer and information science, Linkoping university ,Sweden,issn-0345-7524.
- Kalpakis, K. Gada, Puttagunta, 2001, Distance measure for effective clustering of ARIMA time series . In the proceeding of the 2001, IEEE international conference on data mining ,San Jose ,CA, Nov-29,dec 2, pp273-280
- Geurt,P,2001, Pattern extraction for time series classification , In proceedings of the 5th European conference on principles of data mining and knowledge discovery,sept3 -7,Germany, pp 115-127.
- lin,J, Keogh,E, Lonrdi, S & Patel, 2002, Finding motifs in time series ,In the proceedings of the 2nd workshop on temporal data mining at the 8th ACM SIGKDD, International conference on knowledge discovery and data mining ,Alberta, Canada, PP 53-68
- Dasgupta, D & Forrest,1996, Novelty detection in time series data using ideas from immunology, in the proceeding of the international conference on intelligent systems.
- Everette E Adam,Jr. Ronald J. Ebert,1995,production and operations management ,5th edition, EEE,PHI,ISBN-81-203-0838-7.
- S. Chatterjee,Peihua Qiu,2009, Distribution free cumulative sum control chart using bootstrap control limits,The Annals of applied statistics, volume 3, no 1,349-369,DOI:10. 1214/08-AOAS197.
- Pages downloaded from URL http://en. wikipedia. org/wiki/CUSUM
- Duda,R. O,Hart,1973,Pattern classification and series analysis, Wiley, New York.
- Ge,X & Smith,2001, Segmental semi markov models for end point detection in plasma etching, IEEE transaction on semi conductor engineering.
- Agrawal, R,Psaila,G ,Wimmers,E. L Zait,1995, Querying shapes of histories , In the proceedings of the 21st international conference on very large databases, Zurich, Switzerland, Sept 11-Sept 15, PP 502-514.
- Staden ,1989, Methods for discovering novel motifs in nucleic acid sequences, Computer application in bio sciences, Vol 5, PP293-298.
- Guha, Mishra, Motwani, Callaghan,2000,Clustering data streams, In the proceedings of the 41st symposium on foundations of computer science, Nov 12-14,Rodondo beach, C. A, PP 359-366.
- Huang ,Yu,P. S, 1999, Adaptive query processing for time series data. In the proceedings of the 5th international conference on knowledge discovery and data mining, San Deigo, C. A Aug 15-18,pp 282-286.
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