Kuranga, CryPillay, Nelishia2021-12-062022-03Kuranga, C. & Pillay, N. 2022, 'A comparative study of nonlinear regression and autoregressive techniques in hybrid with particle swarm optimization for time-series forecasting', Expert Systems with Applications, vol. 190, art. 116163, pp. 1-9.0957-4174 (print)1873-6793 (online)10.1016/j.eswa.2021.116163http://hdl.handle.net/2263/82960Usually, real-world time-series forecasting problems are dynamic. If such time-series are characterized by mere concept shifts, a passive approach to learning become ideal to continuously adapt the model parameters whenever new data patterns arrive to cope with uncertainty in the presence of change. This work hybridizes a quantum-inspired particle swarm optimization designed for dynamic environments, to cope with concept shifts, with either a least-squares approximation technique or nonlinear autoregressive model to forecast time-series. Also, this work evaluates experimentally and performs a comparative study on the performance of the proposed models. The obtained results show that the nonlinear autoregressive-based model outperformed the least-squares approximation-based model and the separate models that were implemented in the hybridization and also, several state-of-the-art models for the given datasets.en© 2021 Elsevier Ltd. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Expert Systems with Applications, vol. 190, art. 116163, pp. 1-9, 2022. doi : 10.1016/j.eswa.2021.116163.Time-series forecastingLeast-squaresNonlinear autoregressiveConcept shiftsPassive learningQuantum-inspired particle swarm optimizationPostprint Article