Bagging ensemble-based novel data generation method for univariate time series forecasting
作者：
Highlights：
• We propose a bagging based data generation method for univariate time series forecasting.
• We combine a wavelet transform with bootstrap for generating an ensemble model.
• The proposed method effectively enables univariate time-series data to the ensemble.
• The proposed method overcomes comparative prediction methods.
• The utility of the proposed method was proved through additional experiments.
摘要
•We propose a bagging based data generation method for univariate time series forecasting.•We combine a wavelet transform with bootstrap for generating an ensemble model.•The proposed method effectively enables univariate time-series data to the ensemble.•The proposed method overcomes comparative prediction methods.•The utility of the proposed method was proved through additional experiments.
论文关键词：Time series forecasting,Ensemble method,Bagging,Neural network,Maximum overlap discrete wavelet transform,Data augmentation
论文评审过程：Received 20 August 2021, Revised 17 February 2022, Accepted 25 April 2022, Available online 2 May 2022, Version of Record 13 May 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117366
