A bagging algorithm for the imputation of missing values in time series

作者：

Highlights：

• A new bagging algorithm proposed for imputation of missing values in time series.

• Bagging Kalman filters with auto-ARIMA gives the most accurate imputations.

• Non-overlapping block bootstrap-amplitude modulated processes performs best.

• Optimal block length increases by the length and frequency of the series.

摘要

•A new bagging algorithm proposed for imputation of missing values in time series.•Bagging Kalman filters with auto-ARIMA gives the most accurate imputations.•Non-overlapping block bootstrap-amplitude modulated processes performs best.•Optimal block length increases by the length and frequency of the series.

论文关键词：Block bootstrap,Gap filling,Interpolation,Kalman filter,Stineman,Weighted moving average

论文评审过程：Received 4 December 2018, Revised 26 March 2019, Accepted 26 March 2019, Available online 27 March 2019, Version of Record 3 April 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.03.044