基于谱聚类的高阶模糊时序自适应预测方法

周春楠; 黄少滨; 迟荣华; 李雅; 郎大鹏

doi:10.11959/j.issn.1000-436x.2016036

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基于谱聚类的高阶模糊时序自适应预测方法

学术论文 | 更新时间：2024-06-05

- 基于谱聚类的高阶模糊时序自适应预测方法
- High-order fuzzy time series self-adaption prediction method based on spectral clustering
- 通信学报 2016年37卷第2期页码：107-115
- 作者机构：
  
  哈尔滨工程大学计算机科学与技术学院，黑龙江哈尔滨150001
- 作者简介：
  
  [ "周春楠（1971-），男，黑龙江哈尔滨人，哈尔滨工程大学博士生，主要研究方向为时间序列预测、数据挖掘、不确定性研究等。" ]
  [ "黄少滨（1965-），男，黑龙江哈尔滨人，哈尔滨工程大学教授、博士生导师，主要研究方向为分布式计算与仿真、模型检测、数据集成等。" ]
  [ "迟荣华（1981-），男，黑龙江哈尔滨人，哈尔滨工程大学博士生，主要研究方向为复杂网络、不确定性研究等。" ]
  [ "李雅（1985-），女，黑龙江哈尔滨人，哈尔滨工程大学博士生，主要研究方向为模型监测等。" ]
  [ "郎大鹏（1983-），男，黑龙江哈尔滨人，哈尔滨工程大学博士生，主要研究方向为模型监测等。" ]
- 基金信息：
  
  中央高校基本科研业务专项基金资助项目(HEUCF100603);中央高校基本科研业务费专项基金资助项目(HEUCFZ1212)
- DOI：10.11959/j.issn.1000-436x.2016036
  中图分类号： TP399
- 网络出版日期：2016-02，
  
  纸质出版日期：2016-02-15
- 稿件说明：
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周春楠, 黄少滨, 迟荣华, 等. 基于谱聚类的高阶模糊时序自适应预测方法[J]. 通信学报, 2016,37(2):107-115.

Chun-nan ZHOU, Shao-bin HUANG, Rong-hua CHI, et al. High-order fuzzy time series self-adaption prediction method based on spectral clustering[J]. Journal on communications, 2016, 37(2): 107-115.
周春楠, 黄少滨, 迟荣华, 等. 基于谱聚类的高阶模糊时序自适应预测方法[J]. 通信学报, 2016,37(2):107-115. DOI： 10.11959/j.issn.1000-436x.2016036.

Chun-nan ZHOU, Shao-bin HUANG, Rong-hua CHI, et al. High-order fuzzy time series self-adaption prediction method based on spectral clustering[J]. Journal on communications, 2016, 37(2): 107-115. DOI： 10.11959/j.issn.1000-436x.2016036.

摘要

结合数据特征及分布特点提出一种基于谱聚类的模糊时间序列自适应预测方法。首先基于谱聚类的思想，根据样本数据特征获取其所属论域的个数及范围，实现向模糊时间序列的自适应转化；然后基于 Markov 概率模型表示模糊时间序列中的模糊关系，从而对多步模糊关系、高阶模糊关系及模糊关系的稳态进行求解；最后获取预测值的可能模糊状态，进而利用去模糊化方法将其还原为预测值。在真实以及人工时间序列数据上的实验表明了所提方法的合理性与有效性。

Abstract

A fuzzy time series self-adaption prediction method based on spectral clusterin and data characteristics was proposed. First

based on spectral clustering and the racteristics of data

the number and scope of the discourses was obtained to convert into fuzzy time series self-adaptively. Then

fuzzy relationships based on Markov probability model was presented

and the multi-steps

high-order and steady fuzzy relationship are gotten.Finally

proposed meted obtained the probable fuzzy states

and got its predicted values based on defuzzification methods. Experiments on real-world and synthetic time series data indicate the rationality and effectiveness of the proposed method.

关键词

Keywords

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