Time Series Modeling with Fuzzy Cognitive Maps based on Partitioning Strategies

被引：2

作者：

Feng, Guoliang ^{[1
]}

Lu, Wei ^{[1
]}

Yang, Jianhua ^{[1
]}

机构：

[1] Dalian Univ Technol, Sch Control Sci & Engn, Dalian, Peoples R China

来源：

IEEE CIS INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS 2021 (FUZZ-IEEE) | 2021年

基金：

国家重点研发计划;

关键词：

fuzzy cognitive maps; time series; partitioning strategies; model merging; PREDICTION; ARIMA;

D O I：

10.1109/FUZZ45933.2021.9494479

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The change of amplitude and frequency result in a variety of variation modality of time series in the universe. It is difficult to describe the variation features of time series exactly relying solely on a single simulating model. To overcome this limitation, a new prediction model using fuzzy cognitive maps is proposed based on partitioning strategies. Initially, fuzzy c-mean clustering is adopted to partition time series into several sub-sequences. Consequently, each partition has its corresponding sequences. Subsequently these sub-sequences are used to constructed fuzzy cognitive maps models respectively. Finally, the fuzzy cognitive maps models are merged by fuzzy rules. The constructed model is not only performing well in numerical prediction but also has interpretability. The experimental results show that the model based on partition strategy is superior to the single.

引用

页数：6

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