Open Access
| Issue |
MATEC Web Conf.
Volume 76, 2016
20th International Conference on Circuits, Systems, Communications and Computers (CSCC 2016)
|
|
|---|---|---|
| Article Number | 04015 | |
| Number of page(s) | 4 | |
| Section | Computers | |
| DOI | https://doi.org/10.1051/matecconf/20167604015 | |
| Published online | 21 October 2016 | |
- Akdere M., Cetintemel U., & Upfal E. (2009, August). Database-Support for Continuous Prediction Queries over Streaming Data. Providence, Rhode Island, USA. [Google Scholar]
- Chen A., Leung M., & Daouk H. (2003). Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index. Computers & Operations Research, 901–923. [CrossRef] [Google Scholar]
- Cherewyk P. (2015). Calculating Covariance For Stocks. Retrieved from Investopedia: http://www.investopedia.com/articles/financial-theory/11/calculating-covariance.asp [Google Scholar]
- Economic Research Federal Reserve Bank of St. Louis. 2000. S&P 500© (SP500). [ONLINE] Available at: https://research.stlouisfed.org/fred2/series/SP500/downloaddata. [Accessed 15 July 15]. [Google Scholar]
- Hajari H., & Hakimpour F. (February 2014). A Spatial Data Model For Moving Objects Database. International Journal of Database Management Systems (IJDMS) Vol.6, No.1. [Google Scholar]
- Imfeld S., & P., L. (2002). Analyzing Relative Motion within Groups of rackable Moving Point Objects. Information Systems. [Google Scholar]
- Kara Y., Boyaciglu M. A., & Baykan O. K. (2011). Predicting Direction of Stock Price Index Movement Using Artificial Neural Networks and Support Vector Machines. Expert Systems with Applications, 5311–5320. [CrossRef] [Google Scholar]
- Kays R., Li Z., Ji M., Lee J.-G., Tang L.-A., Yu Y., & Han J. (2011). MoveMine: Mining Moving Objects Database. Illinois. [Google Scholar]
- Kin T., Wu F., Li Z., & Han J. (2013). MoveMine 2.0: Mining Object Relationships From Movement Data. Penssylvania & Illnois. [Google Scholar]
- Li Z., Ding B., Wu F., Lei T. K., Kays R., & Crofoot M. C. (2014). Attraction and Avoidance Detection From Movements. Pennsylvania, USA. [Google Scholar]
- Mahajan K. S., & Kulkarni R. (2013). A Review: Application of Datamining Tools For Stock Market. Kolhapur, India: Institude of Business Education & Research Center. [Google Scholar]
- Olaniyi A. S., Adewole K. S., & Jimoh R. G. (2011). Stock Trend Prediction Using Regression Analysis: A Data Mining Approach. ARPN Journal of Systems and Software, 4. [Google Scholar]
- Pena J. H., & Santos M. Y. (2011). Representing, Storing and Mining Moving Objects Data. World Congress on Engineering. London: WCE. [Google Scholar]
- Stock Charts. (2015). Technical Indicators and Overlays. Retrieved from Stock Charts: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:correlation_coeffici [Google Scholar]
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