Simulation of Time Series Prediction Based on Smooth Support Vector Regression

Authors:
Chao Zhang;Pu Han;Guiji Tang;Guori Ji
Affiliations:
Department of Mechanical Engineering and The Key Laboratory of Condition Monitoring & Control for Power Plant Equipments of Education, North China Electricity Power University, 071003 Baoding, Heb ...;Department of Mechanical Engineering and The Key Laboratory of Condition Monitoring & Control for Power Plant Equipments of Education, North China Electricity Power University, 071003 Baoding, Heb ...;Department of Mechanical Engineering and The Key Laboratory of Condition Monitoring & Control for Power Plant Equipments of Education, North China Electricity Power University, 071003 Baoding, Heb ...;Department of Mechanical Engineering and The Key Laboratory of Condition Monitoring & Control for Power Plant Equipments of Education, North China Electricity Power University, 071003 Baoding, Heb ...
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

Citing 6
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Nonlinear modelling and prediction with feedforward and recurrent networks

Physica D
Making large-scale support vector machine learning practical

Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization

Advances in kernel methods
SSVM: A Smooth Support Vector Machine for Classification

Computational Optimization and Applications
Successive overrelaxation for support vector machines

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Time series analysis and prediction is an important means of dynamic system modelling, but traditional methods of time series prediction such as statistics and artificial neural network (ANN) are not fit for complicated non-linear system. Hence, a new method of support vector regression (SVR) was introduced to solve the prediction problem of complicated time series. For the purpose of reducing complexity of calculation, smooth arithmetic based on SVR was imported to forecast the time series of vibration data collected from turbine system. The result of simulation indicated that smooth support vector regression (SSVR) is obviously superior to ANN method on performance of prediction. Compared with SVR, SSVR has faster speed of convergence and higher fitting precision, which effectively extends the application of support vector machine.Keywords:time series prediction, support vector machine, regression, smooth method, turbine.