Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/36964
Type: Conference paper
Title: Self-projecting time series forecast - An online stock trend forecast system
Author: Deng, K.
Shen, H.
Citation: Parallel and distributed processing and applications : international symposium, ISPA 2003, Aizu-Wakamatsu, Japan, July 2-4, 2003 : proceedings / Minyi Guo, Laurence Tianruo Yang (eds.), pp. 28-43
Publisher: Springer
Publisher Place: Berlin
Issue Date: 2003
Series/Report no.: Lecture notes in computer science; 2745
ISBN: 3540405232
ISSN: 0302-9743
1611-3349
Conference Name: ISPA 2003 (2003 : Aizuwakamatsu-shi, Japan)
Statement of
Responsibility: 
Ke Deng, Hong Shen
Abstract: This paper explores the applicability of time series analysis for stock trend forecast and presents the Self-projecting Time Series Forecasting (STSF) System we have developed. The basic idea behind this system is online discovery of mathematical formulas 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 were 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. Keywords: Self-projecting, forecast, Box-Jenkins methodology, ARIMA, time series, linear transfer function.
Description: The original publication is available at www.springerlink.com
RMID: 0020065796
Appears in Collections:Computer Science publications

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