CAJ | 학술논문

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。
시간서렬적예측재현금사회각개영역중유착엄범적응용。본문침대시간서렬추세예측중적다보예측문제，제출료기우취류적은마이과부모형，이용은마이과부모형중적은상태래표시산생시간서렬수거시적계통내부상태，실현대다보시간서렬적예측。침대시간서렬취류중적거리계산문제，제출결합시간서렬시간성화상사성적취류산법，병급출료질대정화기우취류적은마이과부모형적방법。실험표명，본문제출적방법재시간서렬다보예측중정도교고。
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 .