Linear dynamic fuzzy granule based long-term forecasting model of interval-valued time series

Abstract

The studies on modeling and analysis of time series based on fuzzy granulation have shown that fuzzy granulation is an effective approach in data mining of time series. However, the investigation of fuzzy granulation of interval-valued time series (ITS) and its applications has just begun in recent years with appearance of few research results. Different from the existing studies, this paper carried out the investigation of fuzzy granulation of ITS in interval number space instead of real number space. Two distinguished concepts, namely static fuzzy information granules and dynamic fuzzy information granules of ITS, are proposed firstly. Then the approaches for constructing a static fuzzy information granule and a dynamic fuzzy information granule are designed respectively. After that, two fuzzy granulation methods for ITS based on the above two approaches are presented in the framework of interval analysis under the guidance of the principle of justifiable granularity. Based on the specific proposed fuzzy granule which is called linear dynamic fuzzy granule, a long-term forecasting model for ITS was developed with the aid of artificial neural network. Experiments conducted on several ITS from stock markets with different dynamic characteristics showed the outperformance of the proposed long-term forecasting model.