Robust Time Series Forecasting Using Fuzzy Inference Systems

摘要：

This paper aims to develop a framework of fuzzy systems for robust time-series forecasting. An improved fuzzy rule extraction algorithm using data mining concept is employed to make the resulting fuzzy system be more robust with respect to the input noises or outliers. The proposed technique in this paper is examined with comprehensive robustness analysis by a classical benchmark time-series forecasting problem: the Mackey-Glass time series. Results and comparisons show that the method performs favorably in terms of both accuracy and robustness.

关键词： Time series robustness fuzzy rule fuzzy inference system data mining

作者: BAI Yiming LI Tieshan

作者单位: Navigation College, Dalian Maritime University, Dalian 116026, China

会议类型: 国际会议

会议名称: The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

会议地点: 太原

会议语种:英文

页码: 2715-2718

在线出版日期: 2012-05-23（万方平台首次上网日期，不代表论文的发表时间）