Title :
Stock Price Time Series Prediction using Neuro-Fuzzy with Support Vector Guideline System
Author :
Meesad, Phayung ; Srikhacha, Tong
Author_Institution :
Fac. of Tech. Educ., Dept. of Teacher Training in Electr. Eng., King Mongkuts Inst. of Technol., Bangkok
Abstract :
Global prediction techniques such as support vector machines show accurate prediction for time series data; however, such models tend to delay the predicted output. Fuzzy systems have benefits in local optimum, thus producing significant results within training sets. Unfortunately, the existing techniques sometimes give undesired effects of surface oscillation at predicted outputs. This paper presents a cascade model called Neuro-Fuzzy with Support Vector guideline system (NFSV) to resolve the problem mentioned above. The proposed model takes benefits from both support vector machine and fuzzy model with appropriate stock price rule filtering. From evaluation, the proposed method seems to have low error rate in stock price time series prediction.
Keywords :
economic forecasting; fuzzy neural nets; learning (artificial intelligence); pricing; stock markets; support vector machines; time series; cascade model; neuro-fuzzy model; stock price time series prediction; support vector guideline system; support vector machine; surface oscillation; Artificial intelligence; Distributed computing; Educational technology; Fuzzy systems; Guidelines; Information technology; Predictive models; Software engineering; Support vector machines; Systems engineering education; NFSV; Neuro Fuzzy; Prediction; Stock; Support Vector; Time Series;
Conference_Titel :
Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008. SNPD '08. Ninth ACIS International Conference on
Conference_Location :
Phuket
Print_ISBN :
978-0-7695-3263-9
DOI :
10.1109/SNPD.2008.55