A heuristic method for parameter selection in LS-SVM: Application to time series prediction

被引：76

作者：

Rubio, Gines ^{[1
]}

Pomares, Hector ^{[1
]}

Rojas, Ignacio ^{[1
]}

Javier Herrera, Luis ^{[1
]}

机构：

[1] Univ Granada, Dept Comp Architecture & Comp Technol, E-18071 Granada, Spain

来源：

INTERNATIONAL JOURNAL OF FORECASTING | 2011年 / 27卷 / 03期

关键词：

Least squares support vector machines; Gaussian kernel parameters; Hyperparameters optimization; Time series prediction; SUPPORT VECTOR MACHINES; NEURAL-NETWORK MODELS; REGRESSION; ARIMA; ALGORITHMS; SYSTEM;

D O I：

10.1016/j.ijforecast.2010.02.007

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Least Squares Support Vector Machines (LS-SVM) are the state of the art in kernel methods for regression. These models have been successfully applied for time series modelling and prediction. A critical issue for the performance of these models is the choice of the kernel parameters and the hyperparameters which define the function to be minimized. In this paper a heuristic method for setting both the a parameter of the Gaussian kernel and the regularization hyperparameter based on information extracted from the time series to be modelled is presented and evaluated. (C) 2010 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

引用

页码：725 / 739

页数：15

共 54 条

[1] Automatic identification of time series features for rule-based forecasting [J].

Adya, M ;

Collopy, F ;

Armstrong, JS ;

Kennedy, M .

INTERNATIONAL JOURNAL OF FORECASTING, 2001, 17 (02) :143-157

[2]

Adya M, 1998, J FORECASTING, V17, P481, DOI 10.1002/(SICI)1099-131X(1998090)17:5/6<481::AID-FOR709>3.3.CO

[3]

2-H

[4] Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression [J].

An, Senjian ;

Liu, Wanquan ;

Venkatesh, Svetha .

PATTERN RECOGNITION, 2007, 40 (08) :2154-2162

[5]

[Anonymous], 2004, WORLD C INT CONTR AU

[6]

[Anonymous], 2018, TIME SERIES PREDICTI

[7] Automatic neural network modeling for univariate time series [J].

Balkin, SD ;

Ord, JK .

INTERNATIONAL JOURNAL OF FORECASTING, 2000, 16 (04) :509-515

[8]

Bellman R., 1966, SIAM J. Control, V4, P1

[9] An empirical investigation of bias and variance in time series forecasting: Modeling considerations and error evaluation [J].

Berardi, VL ;

Zhang, GP .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2003, 14 (03) :668-679

[10]

Box G.E.P., 1976, Time Series Analysis: Forecasting and Control

← 1 2 3 4 5 6 →