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

作者：

Highlights：

• QPSO-based technique induces an adaptive prediction model in a dynamic environment.

• QPSO-based technique hybridizes a QPSO with either a QR decomposition or a NARX.

• QPSO-based technique is evaluated on six time-series datasets.

• The performance of NARX-QPSO is competitive to several state-of-the-art techniques.

摘要

•QPSO-based technique induces an adaptive prediction model in a dynamic environment.•QPSO-based technique hybridizes a QPSO with either a QR decomposition or a NARX.•QPSO-based technique is evaluated on six time-series datasets.•The performance of NARX-QPSO is competitive to several state-of-the-art techniques.

论文关键词：Time-series forecasting,Least-squares,Nonlinear autoregressive,Concept shifts,Passive learning,Quantum-inspired particle swarm optimization

论文评审过程：Received 31 October 2020, Revised 3 October 2021, Accepted 27 October 2021, Available online 9 November 2021, Version of Record 12 November 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116163