Online prediction of time series data with recurrent kernels

Online prediction of time series data with recurrent kernels

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 gener...

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Bibliographic Details
Main Authors: Xu, Zhao, Song, Qing, Haijin, Fan, Wang, Danwei
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2013
Subjects:
DRNTU::Engineering::Electrical and electronic engineering
Online Access:https://hdl.handle.net/10356/98301
http://hdl.handle.net/10220/12420
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.

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