Title: Self projecting time series forecast: an online stock trend forecast system

Authors: Ke Deng, Hong Shen, Hui Tian

Addresses: School of Information Technology and Electrical Engineering, University of Queensland, St. Lucia, QLD 4072, Australia. ' Graduate School of Information Science, Japan Advanced Institute of Science and Technology, Tasunokuchi, Ishikawa, 923 1292 Japan. ' Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, M1 5GD, UK

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.

Keywords: self projecting forecasting; forecasts; Box-Jenkins methodology; arima; time series analysis; linear transfer function; stock trends.

DOI: 10.1504/IJCSE.2006.009934

International Journal of Computational Science and Engineering, 2006 Vol.2 No.1/2, pp.46 - 56

Published online: 03 Jun 2006 *

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