Time-series interval prediction under uncertainty using modified double multiplicative neuron network
作者：
Highlights：
• A hybrid approach for prediction intervals (PIs) estimation of terrain profiles is proposed.
• It integrates the double multiplicative neuron (DMN) model and the modified PSO (MPSO).
• MPSO adjusts parameters of DMN by minimizing the value of the proposed cost function.
• It improves the prediction accuracy for terrain trends by 15.4% in the testing data,
• It reduces the computational burden by 8% in the testing data over the LUBE method.
摘要
•A hybrid approach for prediction intervals (PIs) estimation of terrain profiles is proposed.•It integrates the double multiplicative neuron (DMN) model and the modified PSO (MPSO).•MPSO adjusts parameters of DMN by minimizing the value of the proposed cost function.•It improves the prediction accuracy for terrain trends by 15.4% in the testing data,•It reduces the computational burden by 8% in the testing data over the LUBE method.
论文关键词：Multiplicative neuron,Particle swarm optimization,Prediction intervals,Terrain profiles,Time series prediction
论文评审过程：Received 5 October 2020, Revised 3 March 2021, Accepted 23 June 2021, Available online 27 June 2021, Version of Record 2 July 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115478
