Predicting Time Series Using Incremental Langrangian Support Vector Regression

Authors:
Hua Duan;Weizhen Hou;Guoping He;Qingtian Zeng
Affiliations:
Department of Mathematics, Shanghai Jiaotong University, Shanghai 200240, P.R. China and College of Information Science and Engineering, Shandong University of Science and, Technology, Qingdao 266 ...;College of Information Science and Engineering, Shandong University of Science and, Technology, Qingdao 266510, China;College of Information Science and Engineering, Shandong University of Science and, Technology, Qingdao 266510, China;College of Information Science and Engineering, Shandong University of Science and, Technology, Qingdao 266510, China
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
Support-Vector Networks

Machine Learning
Using support vector machines for time series prediction

Advances in kernel methods
Predicting Time Series with Support Vector Machines

ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
SVMTorch: support vector machines for large-scale regression problems

The Journal of Machine Learning Research
Lagrangian support vector machines

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel Support Vector Regression(SVR) algorithm has been proposed recently by us. This approach, called Lagrangian Support Vector Regression(LSVR), is an reformulation on the standard linear support vector regression, which leads to the minimization problem of an unconstrained differentiable convex function. During the process of computing, the inversion of matrix after incremented is solved based on the previous results, therefore it is not necessary to relearn the whole training set to reduce the computation process. In this paper, we implemented the LSVR and tested it on Mackey-Glass time series to compare the performances of different algorithms. According to the experiment results, we achieve a high-quality prediction about time series.