Online prediction of time series data with recurrent kernels

Zhao Xu, Qing Song, Fan Haijin, Danwei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

We propose a robust recurrent kernel online learning (RRKOL) algorithm which allows the exploitation of the kernel trick in an online fashion. The novel RRKOL algorithm achieves guaranteed weight convergence with regularized risk management through the recurrent hyper-parameters for a superior generalization performance. To select useful data to be learned and remove redundant ones, a sparcification procedure is developed based on the stability analysis of the system. Two time-series prediction examples are presented.

Original languageEnglish
Title of host publication2012 International Joint Conference on Neural Networks, IJCNN 2012
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia
Duration: 10 Jun 201215 Jun 2012

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
Country/TerritoryAustralia
CityBrisbane, QLD
Period10/06/1215/06/12

Fingerprint

Dive into the research topics of 'Online prediction of time series data with recurrent kernels'. Together they form a unique fingerprint.

Cite this