relation: https://khub.utp.edu.my/scholars/5604/
title: Scaled UKF�NARX hybrid model for multi-step-ahead forecasting of chaotic time series data
creator: Abdulkadir, S.J.
creator: Yong, S.-P.
description: Accurate forecasting is critically important in many time series applications. In this paper, we consider forecasting chaotic problems by proposing a hybrid model composed of scaled unscented Kalman filter with reduced sigma points and non-linear autoregressive network with exogenous inputs, trained using a modified Bayesian regulation backpropagation algorithm. To corroborate developments of the proposed hybrid model, real-life chaotic and simulated time series which are both non-linear in nature are applied to validate the proposed hybrid model. Experiment results show that the proposed hybrid model outperforms other forecasting models reported in the literature in forecasting of chaotic time series. © 2015, Springer-Verlag Berlin Heidelberg.
publisher: Springer Verlag
date: 2015
type: Article
type: PeerReviewed
identifier:   Abdulkadir, S.J. and Yong, S.-P.  (2015) Scaled UKF�NARX hybrid model for multi-step-ahead forecasting of chaotic time series data.  Soft Computing, 19 (12).  pp. 3479-3496.  ISSN 14327643     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84947127920&doi=10.1007%2fs00500-015-1833-z&partnerID=40&md5=bac3037dcc6506253e1ce33dde90eb2c
relation: 10.1007/s00500-015-1833-z
identifier: 10.1007/s00500-015-1833-z