电子学报 ›› 2014, Vol. 42 ›› Issue (12): 2359-2364.DOI: 10.3969/j.issn.0372-2112.2014.12.004

• 学术论文 • 上一篇    下一篇

针对时间序列多步预测的聚类隐马尔科夫模型

章登义, 欧阳黜霏, 吴文李   

  1. 武汉大学计算机学院, 湖北武汉 430072
  • 收稿日期:2014-03-19 修回日期:2014-06-19 出版日期:2014-12-25
    • 通讯作者:
    • 欧阳黜霏
    • 作者简介:
    • 章登义 男,1965年5月出生于湖北省荆州市.现为武汉大学计算机学院教授、博士生导师. E-mail:dyzhangwhu@163.com;吴文李 女,1986年12月出生于广东湛江.现为武汉大学博士研究生.主要研究语义网数据挖掘. E-mail:huihuigou@whu.edu.cn
    • 基金资助:
    • 国家自然科学基金 (No.60903035,No.41001296); 国家高技术研究发展计划 (863计划)课题 (No.2013AA12A301)

Cluster-Based Hidden Markov Model in Time Series Multi-Step Prediction

ZHANG Deng-yi, OUYANG Chu-fei, WU Wen-li   

  1. School of Computer, Wuhan University, Wuhan, Hubei 430072, China
  • Received:2014-03-19 Revised:2014-06-19 Online:2014-12-25 Published:2014-12-25
    • Supported by:
    • National Natural Science Foundation of China (No.60903035, No.41001296); Subject of National High-tech R&D Program of China  (863 Program) (No.2013AA12A301)

摘要:

时间序列的预测在现今社会各个领域中有着广泛的应用.本文针对时间序列趋势预测中的多步预测问题,提出了基于聚类的隐马尔科夫模型,利用隐马尔科夫模型中的隐状态来表示产生时间序列数据时的系统内部状态,实现对多步时间序列的预测.针对时间序列聚类中的距离计算问题,提出结合时间序列时间性和相似性的聚类算法,并给出了迭代精化基于聚类的隐马尔科夫模型的方法.实验表明,本文提出的方法在时间序列多步预测中精度较高.

关键词: 时间序列, 多步预测, 隐马尔科夫模型, 聚类

Abstract:

The study of time series prediction is pervasive in various fields.We propose a cluster-based hidden Markov model to approach the multi-step prediction problem in time series.As multi-step time series prediction problem is not fully addressed from a system angle,we utilize the hidden state of hidden Markov model to represent the inner state of a time series production system.We also promote a cluster algorithm combining the temporal and similarity criteria to address the distance calculating issue in time series clustering.This non-trivial criterion proves effective in multi-step time series prediction.Through a non-parameter approximate method we estimate the inner hidden state distributes from every single state.And we also prove the correctness of an iteratively refinement of the cluster-based hidden Markov model(HMM).Experimental results on authentic data indicate the effectiveness and accuracy of this approach.

Key words: time series, multi-step prediction, hidden Markov model(HMM), cluster

中图分类号: