Title :
Multivariate time series forecasting based on BP-SVR
Author :
Fan, Xinwei ; Li, Fengyuan
Author_Institution :
Coll. of Quality & Safety Eng., China Jiliang Univ., Hangzhou, China
Abstract :
The characteristics of financial time series : (1)the selection process is random, complex; (2) contain most of the noise; (3) between the data with strong non-linear. The traditional prediction technologies cannot disclose the inherent rule of stock market. In this paper briefly introduces the basic theory of Support Vector Regress (SVR), and applies SVR combined with neural network (BP-SVR) to create a model, which also can be used for forecasting the multivariate time series. The result of simulation shows that the new model is the least in the mean squared error, which demonstrates that the BP-SVR model has a good ability to generalize.
Keywords :
backpropagation; economic forecasting; mean square error methods; neural nets; regression analysis; support vector machines; time series; BP-SVR model; financial time series; mean squared error; multivariate time series forecast; neural network; prediction technology; stock market; support vector regression; Artificial neural networks; Computer languages; Forecasting; Statistical learning; Stock markets; Support vector machines; Time series analysis; Data mining; Support Vector Regress (SVR); neural network; time Series;
Conference_Titel :
Computer Science and Service System (CSSS), 2011 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-9762-1
DOI :
10.1109/CSSS.2011.5974111
