Comparative analysis of properties of weakening buffer operators in time series prediction models

Date

2018-08-23

Advisors

Journal Title

Journal ISSN

ISSN

1007-5704

Volume Title

Publisher

Elsevier

Type

Article

Peer reviewed

Yes

Abstract

Reducing the negative influence of stochastic disturbances in sample data has always been a difficult problem in time series analysis. In this paper, three new fractional weakening buffer operators are proposed, and then some desirable properties of these proposed se- quence operators are investigated. Their potential effect in smoothing unexpected distur- bances while maintaining the normal trend in sample series is analyzed and compared with other widely used sequence operators in time series modeling. Results of theoretical and empirical research show that the proposed novel fractional weakening buffer oper- ators are effective in improving the development pattern analysis of time series in dis- turbance scenarios, while also avoid too subjectively weighting experimental data from collected samples. The robust of the proposed operator-based prediction algorithm against noise effect is tested in five different types of noise scenarios. Result of empirical study demonstrates that the proposed method improves the series prediction performance and it also improves the robustness of corresponding forecasting algorithms. These unique prop- erties of the proposed weakening buffer operators make them more attractive in time se- ries analysis.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Weakening buffer operator, Time series smoothness, Data disturbances, Trend prediction

Citation

Li, C., Yang, Y. and Liu, S. (2019) Comparative analysis of properties of weakening buffer operators in time series prediction models. Communications in Nonlinear Science and Numerical Simulation, 68, pp.257-285.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)