针对时间序列多步预测的聚类隐马尔科夫模型

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.

Cluster-Based Hidden Markov Model in Ti me Series Multi-Step Prediction

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.