Title :
On-demand forecasting of stock prices using a real-time predictor
Author_Institution :
Dept. of Inf. Manage., Chang Gung Inst. of Technol., Taoyuan, Taiwan
Abstract :
This paper presents a fuzzy stochastic prediction method for real-time predicting of stock prices. A complete contrast to the crisp stochastic method, it requires a fuzzy linguistic summary approach to computing parameters. This approach, which is found to be better than the gray prediction method, can eliminate outliers and limit the data to a normal condition for prediction, with a comparatively very small deviation of 4.5 percent.
Keywords :
costing; forecasting theory; fuzzy systems; real-time systems; stochastic processes; stock markets; crisp stochastic method; fuzzy linguistic summary approach; fuzzy stochastic prediction method; gray prediction method; on-demand stock price forecasting; real-time predictor; Application software; Artificial neural networks; Computer crashes; Fluctuations; Fuzzy sets; Prediction methods; Predictive models; Stochastic processes; Stochastic resonance; Vehicle crash testing;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
DOI :
10.1109/TKDE.2003.1209017
