Support Vector Machine for Predicting Stock Price Based on RBF KernelRBF커널 기반 주가 예측 벡터머신
- Other Titles
- RBF커널 기반 주가 예측 벡터머신
- Authors
- Li, Xintao; Lee, Scott Uk-Jin
- Issue Date
- Jun-2017
- Publisher
- 한국정보과학회
- Citation
- 한국정보과학회 2017 한국컴퓨터종합학술대회 논문집, v.2017, no.06, pp.856 - 858
- Indexed
- OTHER
- Journal Title
- 한국정보과학회 2017 한국컴퓨터종합학술대회 논문집
- Volume
- 2017
- Number
- 06
- Start Page
- 856
- End Page
- 858
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/9546
- ISSN
- 24660825
- Abstract
- In current stock market,people are concerned more about the stock price prediction in recent years due to price fluctuation in the stock market and rapidly changing the prices. Therefore, in this paper we have proposed a price prediction solution by using Support Vector Machine(SVM) and Radial Basis Function(RBF) kernel to predict and the price of the stock from the previous values. To be exact, comparing with different parameters which are used in this model, we can get a set of optimum parameters to fit the regression and predict which is very close to the real value i.e. Price. We have applied our proposed approach on real time existing cases of stock market to ensure the correctness of results. Our experimental results show that percentage of the prediction success more than 70% which is very close to the previously existing values.
- Files in This Item
-
Go to Link
- Appears in
Collections - COLLEGE OF COMPUTING > ERICA 컴퓨터학부 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/9546)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.