Paper Info
Title
A Novel Forecasting Model for the Long-term Fluctuation of Time Series based on Polar Fuzzy Information Granules
Abstract
The long-term fluctuation of time series is generally composed of a large number of short-term behaviors with various dynamical characteristics, where kinds of fluctuation patterns in different periods change mutually. In this paper, we propose a novel method to construct fuzzy information granules in polar coordinates and achieve the prediction of long-term fluctuation of time series on the basis of the short-term fluctuation patterns. Firstly, time series are divided into segments by means of the sliding time windows, and fuzzy information granules are defined based on the regression models to indicate the fluctuation patterns of segments of time series. The transfers among different information granules form a dynamical network containing rich inference information. Next, the constructed networks are analyzed to capture the transfer characteristics of fuzzy information granules. The results show that only a few types of fuzzy information granules and fuzzy relation groups play the key role in the fluctuation mechanism, which always have specific targets. Hence, according to the distribution of the transfer probability, a prediction scheme on the granularity level can be established. By utilizing both synthetic and real-life data sets, examples are shown to illustrate the effectiveness and feasibility of the proposed scheme.
Year
DOI
Venue
2020
10.1016/j.ins.2019.10.020
Information Sciences
Keywords
Field
DocType
Fuzzy information granules,Fuzzy inference system,Transfer networks,Financial time series
Data set,Inference,Regression analysis,Fuzzy logic,Algorithm,Granule (cell biology),Polar coordinate system,Artificial intelligence,Polar,Granularity,Machine learning,Mathematics
Journal
Volume
ISSN
Citations 
512
0020-0255
3
PageRank 
References 
Authors
0.37
0
3
Name
Order
Citations
PageRank
Chao Luo15817.22
Xi Song230.71
Yuanjie Zheng3124.99