Title :
Study on data stream prediction based on least square support vector machine
Author_Institution :
Modern Educ. Tech. Dept., Minzu Univ. of China, Beijing, China
Abstract :
Recently, data stream prediction has become a new hotspot in the field of data processing. Support vector machine is a new forecasting method. It can better solve the small sample, nonlinear, high dimension and local minimum points and other practical problems. In the paper, Sliding window processing mode and incremental online least square support vector machine(LS-SVM) algorithm was introduced to the data flow forecast. The experiment uses the time series data set of gas furnace of Box-Jenkins to validate the effectiveness in the accurate and response time.
Keywords :
data flow computing; data mining; learning (artificial intelligence); least mean squares methods; support vector machines; time series; LS-SVM algorithm; data flow forecasting; data stream prediction; incremental online learning; least square support vector machine; sliding window processing mode; Equations; Fitting; Forecasting; Kernel; Mathematical model; Prediction algorithms; Support vector machines; data stream; least square support vector machine; online learning; prediction; regression mode;
Conference_Titel :
Uncertainty Reasoning and Knowledge Engineering (URKE), 2011 International Conference on
Conference_Location :
Bali
Print_ISBN :
978-1-4244-9985-4
Electronic_ISBN :
978-1-4244-9984-7
DOI :
10.1109/URKE.2011.6007890
