Self Projecting Time Series Forecast: an Online Stock Trend Forecast System

Deng, K, Shen, H.T. and Tian, H (2006) Self Projecting Time Series Forecast: an Online Stock Trend Forecast System. International Journal of Computational Science and Engineering, 2 1/2: 46-56. doi:10.1504/IJCSE.2006.009934

Author Deng, K
Shen, H.T.
Tian, H
Title Self Projecting Time Series Forecast: an Online Stock Trend Forecast System
Journal name International Journal of Computational Science and Engineering   Check publisher's open access policy
ISSN 1742-7185
Publication date 2006
Sub-type Article (original research)
DOI 10.1504/IJCSE.2006.009934
Volume 2
Issue 1/2
Start page 46
End page 56
Total pages 11
Publisher Inderscience Enterprises Limited
Language eng
Subject 150301 Business Information Management (incl. Records, Knowledge and Information Management, and Intelligence)
080708 Records and Information Management (excl. Business Records and Information Management)
Abstract This paper explores the applicability of time series analysis for stock trend forecast and presents the Self projecting Time Series Forecasting (STSF) System which we have developed. The basic idea behind this system is the online discovery of mathematical formulae that can approximately generate historical patterns from given time series. SPTF offers a set of combined prediction functions for stocks, including Point Forecast and Confidence Interval Forecast, where the latter could be considered as a subsidiary index of the former in the process of decision making. We propose a new approach to determine the support line and resistance line that are essential for market assessment. Empirical tests have shown that the hit rate of the prediction is impressively high if the model is properly selected, indicating a good accuracy and efficiency of this approach. The numerical forecast result of STSF is superior to normal descriptive investment recommendation offered by most web brokers. Furthermore, SPTF is an online system and investors and analysts can upload their real time data to get the forecast result on the web.
Keyword self projecting forecasting
Box-Jenkins methodology
Time series analysis
Linear transfer function
Stock trends
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: Scopus Citation Count Cited 1 times in Scopus Article | Citations
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Created: Thu, 09 Apr 2009, 08:55:18 EST by Ms Sarada Rao on behalf of School of Information Technol and Elec Engineering