电子学报Issue(12)：2359-2364,6.DOI:10.3969/j.issn.0372-2112.2014.12.004
针对时间序列多步预测的聚类隐马尔科夫模型
Cluster-Based Hidden Markov Model in Ti me Series Multi-Step Prediction
摘要
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 re-finement of the cluster-based hidden Markov model(HMM).Experimental results on authentic data indicate the effectiveness and accuracy of this approach .关键词
时间序列/多步预测/隐马尔科夫模型/聚类Key words
time series/multi-step prediction/hidden Markov model(HMM)/cluster分类
信息技术与安全科学引用本文复制引用
章登义,欧阳黜霏,吴文李..针对时间序列多步预测的聚类隐马尔科夫模型[J].电子学报,2014,(12):2359-2364,6.基金项目
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 （）
