A comparative study of nonlinear regression and autoregressive techniques in hybrid with particle swarm optimization for time-series forecasting

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Authors

Kuranga, Cry
Pillay, Nelishia

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Publisher

Elsevier

Abstract

Usually, 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.

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Keywords

Time-series forecasting, Least-squares, Nonlinear autoregressive, Concept shifts, Passive learning, Quantum-inspired particle swarm optimization

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Citation

Kuranga, 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.