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Fuzzy forecasting for long-term time series based on time-variant fuzzy information granules.
- Source :
- Applied Soft Computing; Mar2020, Vol. 88, pN.PAG-N.PAG, 1p
- Publication Year :
- 2020
-
Abstract
- As to the long-term time series forecasting, it is more challenging and practical to obtain the trend information and fluctuation range of sequence data than single-step prediction values. In this article, by means of fuzzy information granules (FIGs) and recurrent fuzzy neural networks, a novel long-term prediction model for time series is proposed. Based on a variable-length division method, generalized zonary time-variant fuzzy information granule (GZT-FIG) is constructed, which can express the variation trend, fluctuation range and dispersion degree of sequence data. Furthermore, in order to improve the anti-noise ability and memory ability, type-2 fuzzy sets and long short-term memory mechanism are introduced into the prediction scheme, based on which a self-evolving interval type-2 LSTM fuzzy neural network (eIT2FNN-LSTM) is provided. Compared with the existing works related to fuzzy-neural models, the involvement of long short-term memory mechanism effectively improves the memory ability to achieve the long-term prediction. In order to verify the validation and effectiveness of the proposed scheme, several groups of experiments, including synthetic sequences and real-life time series, are carried out. The experimental results reveal the better predictive performance and rich semantic information. • A method of unequal dividing the time series based on stepwise linear division is proposed. • A generalized time-variant fuzzy information granule can reflect the trend and the range of data changing. • A distance algorithm to measure the similarity and characterize the correlation degree between two GZT-FIGs. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15684946
- Volume :
- 88
- Database :
- Supplemental Index
- Journal :
- Applied Soft Computing
- Publication Type :
- Academic Journal
- Accession number :
- 141829929
- Full Text :
- https://doi.org/10.1016/j.asoc.2019.106046