caa a22 4500 605469245 CHVBK 20210128100321.0 cr unu---uuuuu 210128e20151201xx s 000 0 eng 10.1007/s00500-015-1833-z doi (NATIONALLICENCE)springer-10.1007/s00500-015-1833-z Scaled UKF-NARX hybrid model for multi-step-ahead forecasting of chaotic time series data [Elektronische Daten] [Said Abdulkadir, Suet-Peng Yong] 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. Springer-Verlag Berlin Heidelberg, 2015 Chaotic time series nationallicence NARX nationallicence Hybrid model nationallicence Multi-step-ahead forecasting nationallicence Scaled UKF-NARX nationallicence Abdulkadir Said Department of Computer and Information Sciences, Universiti Teknologi Petronas (UTP), 31750, Tronoh, Malaysia aut Yong Suet-Peng Department of Computer and Information Sciences, Universiti Teknologi Petronas (UTP), 31750, Tronoh, Malaysia aut Soft Computing Springer Berlin Heidelberg 19/12(2015-12-01), 3479-3496 1432-7643 19:12<3479 2015 19 500 https://doi.org/10.1007/s00500-015-1833-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-015-1833-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Abdulkadir Said Department of Computer and Information Sciences, Universiti Teknologi Petronas (UTP), 31750, Tronoh, Malaysia aut NATIONALLICENCE 700 1- Yong Suet-Peng Department of Computer and Information Sciences, Universiti Teknologi Petronas (UTP), 31750, Tronoh, Malaysia aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/12(2015-12-01), 3479-3496 1432-7643 19:12<3479 2015 19 500