基于连续密度隐马尔可夫的时间序列分类算法

针对数据挖掘过程中对异常数据检测的准确率较低、分类速度较慢,导致数据分类准确率较低、效率较差的问题,提出基于连续密度隐马尔可夫的时间序列分类算法.构建时间序列变化趋势分割点目标函数,利用贪婪搜索法求解时间序列分段值,提取序列变化趋势特征得到数据主要信息,提升数据分类的准确性;改进帧内特征表达准确性,使用因子分析矩阵高斯...

Time Series Classification Algorithm Based on Continuous Density Hidden Markov

Aiming at low accuracy and low classification speed of abnormal data detection in the process of data mining,an algorithm of time series classification based on continuous density hidden Markov was proposed.At first,the objective function of segmentation point of time series trend was established,and then the greedy search method was used to solve the piecewise value of time series,and thus to extract the sequence trend features and the main information of data.In this way,the accuracy of data classification was improved.Moreover,the accuracy of intra-feature expression was improved,and the Gaussian distribution of factor analysis matrix was used to build a continuous density hidden Markov model,and thus to improve the classification speed of time series.In addition,the stationary subspace analysis method was used to divide the data into a stationary subspace and a non-stationary subspace.Finally,the relative entropy was used to weigh the similarity of stationary subspace distribution,so as to achieve the accurate classification of time series.Simulation results show that the proposed method has high classification accuracy,fast calculation and good robustness,so it can meet the needs of data analysis in real scenarios.