A robust maximum correntropy forecasting model for time series with outliers

Jing Ren; Wei-Qin Li

doi:10.7717/peerj-cs.1251

A robust maximum correntropy forecasting model for time series with outliers

PeerJ Comput Sci. 2023 Feb 8:9:e1251. doi: 10.7717/peerj-cs.1251. eCollection 2023.

Authors

Jing Ren¹, Wei-Qin Li²

Affiliations

¹ College of Computer, Xi'an Aeronautical Institute, Xi'an, Shaanxi Province, China.
² School of Automation and Information Engineering, Xi'an University of Technology, Xi'an, Shaanxi, China.

Abstract

It is of great significance to develop a robust forecasting method for time series. The reliability and accuracy of the traditional model are reduced because the series is polluted by outliers. The present study proposes a robust maximum correntropy autoregressive (MCAR) forecasting model by examining the case of actual power series of Hanzhong City, Shaanxi province, China. In order to reduce the interference of the outlier, the local similarity between data is measured by the Gaussian kernel width of correlation entropy, and the semi-definite relaxation method is used to solve the parameters in MCAR model. The results show that the MCAR model in comparison with deep learning methods, in terms of the average value of the mean absolute percentage error (MAPE), performed better by 1.63%. It was found that maximum correntropy is helpful for reducing the interference of outliers.

Keywords: Maximum correntropy; Outlier; Regression; Time series forecasting.

Grants and funding

This work was supported by the National Natural Science Foundation of China (No. 61871318). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.