计算机工程2018，Vol.44Issue(1)：56-61,68,7.DOI:10.3969/j.issn.1000-3428.2018.01.009
时间序列数据趋势转折点提取算法
Trend Turning Point Extraction Algorithm for Time Series Data
摘要
Abstract
The time series data contains trend information,which can extract the trend turning point according to the trend information of the data,and can achieve the purpose of compressing the data and reducing the influence of noise.By analyzing the trend information of time series data,an adaptive data trend turning point extraction algorithm is proposed.The algorithm does not rely on any prior knowledge,only according to the trend characteristics of the data itself automatically extract the trend turning point,extracted information including the coordinate index and the corresponding data.Compared with SEEP,CAP and PAA algorithm,experimental results show that the fitting error and classification error rate of the algorithm are smaller in the case of multiple data,and the average fitting error is 0.373 6,the classification error rate compared with the original data classification error rate decreases by 3.39％.关键词
时间序列/趋势转折点/UCR时间序列分类数据集/分段线性表示/拟合误差Key words
time series/trend turning point/UCR time series classification dataset/Piecewise Linear Representation (PLR)/fitting error分类
信息技术与安全科学引用本文复制引用
邢邗,石晓达,孙连英,葛娜..时间序列数据趋势转折点提取算法[J].计算机工程,2018,44(1):56-61,68,7.基金项目
国家重点研发计划项目(2016YFC0802107). （2016YFC0802107）
