A mixture of support vector machines for time series forecasting

Title:

A mixture of support vector machines for time series forecasting

Creator:

Cao, Lijuan and Jingqing , Zhang

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:a7b7d28b-add9-45f7-8336-99e89c85a4c7
uuid:a7b7d28b-add9-45f7-8336-99e89c85a4c7

Subject:

Non-stationarity: support vector machines mixture and self-organizing feature map

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

A mixture of support vector machines (SVMs) is proposed for time series forecasting. The SVMs mixture is composed of a two-stage architecture. In the first stage, self-organizing feature map (SOM) is used as a clustering algorithm to partition the whole input space into several disjointed regions. A tree-structured architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second stage, multiple SVMs, also called SVM mixture, that best fit partitioned regions are constructed by finding the most appropriate kernel function and the optimal free parameters of SVMs. The experiment shows that the SVMs mixture achieves significant improvement in the generalization performance in comparison with the single SVMs model. In addition, the SVMs mixture also converges faster and use fewer support vectors.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2006 Volume:16 | Number:5

Harvested from:

CDK

Metadata only:

false