Self projecting time series forecast: An online stock trend forecast system

Ke Deng, Hong Shen, Hui Tian

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)46-56
Number of pages11
JournalInternational Journal of Computational Science and Engineering
Issue number1-2
Publication statusPublished - 2006
Externally publishedYes


  • Arima
  • Box-Jenkins methodology
  • Forecast
  • Linear transfer function
  • Self projecting
  • Time series


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