基于FCM的时间序列论域划分方法

doi:10.3969/j.issn.1006-2475.2015.05.002

计算机与现代化

基于FCM的时间序列论域划分方法

(河海大学计算机与信息学院，江苏南京 211100)

收稿日期:2015-03-31 出版日期:2015-05-18 发布日期:2015-05-18
作者简介:刘澈(1989-)，男，辽宁阜新人，河海大学计算机与信息学院硕士研究生，研究方向：数据挖掘与预测; 刘璇(1989-)，女，博士研究生，研究方向：大数据，数据挖掘与预测; 马鸿旭(1987-)，男，博士研究生，研究方向：云计算，数据挖掘与预测。

A FCM-based Domain Partition Method for Time Series Data Set

(College of Computer and Information, Hohai University, Nanjing 211100, China)

Received:2015-03-31 Online:2015-05-18 Published:2015-05-18

摘要/Abstract

摘要： 随着社会的发展，人们对于数据预测的需求日益增加，模糊时间序列因其能够处理时间序列中含糊不清的数据而备受关注。从提高模型的预测精度角度来看，论域划分作为时间序列数据预测的第一步，作用至关重要。本文提出一种基于FCM的二次论域划分方法。该方法首先根据FCM聚类算法得到的聚类中心对论域进行一次划分，然后根据样本点空间分布的疏密程度不同对论域进行二次细化，实现不等分论域，最后通过对经典样本的预测证明方法的可行性。

关键词: 时间序列, 论域划分, FCM聚类算法, 数据预测

Abstract: With the development of society, people increasingly demand for data prediction, fuzzy time series has been attracted much attention because it can handle the ambiguities in time series data. To improve the prediction accuracy of the model, domain partition is crucial as the first step of the prediction. In this paper, we present a FCM-based two-time domain partition method. We make the first partition according to the clustering center obtained from FCM clustering, the second partition to realize unequal domain according to the density distribution of the sample points. Finally, the feasibility of the new method is verified through the prediction of classic sample.

Key words: time series, domain partition, FCM clustering algorithm, data forecast

中图分类号:

TP391

刘澈，刘璇，马鸿旭. 基于FCM的时间序列论域划分方法[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2015.05.002.

LIU Che, LIU Xuan, MA Hong-xu. A FCM-based Domain Partition Method for Time Series Data Set[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.05.002.

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基于FCM的时间序列论域划分方法

A FCM-based Domain Partition Method for Time Series Data Set

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