Cluster-Based Hidden Markov Model in Time Series Multi-Step Prediction
Zhang Deng-y · Dianzi xuebao · 2014
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 clusterbased hidden Markov model(HMM).Experimental results on authentic data indicate the effectiveness and accuracy of this approach.