Trend Turning Point Extraction Algorithm for Time Series Data

Abstract

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%.

Key words: time series, trend turning point, UCR time series classification dataset, Piecewise Linear Representation(PLR), fitting error

摘要： 时间序列数据蕴含趋势信息,可以根据数据的趋势信息提取趋势转折点,达到压缩数据、减少噪声影响的目的。通过分析时间序列数据的趋势信息,提出自适应数据趋势转折点提取算法。该算法不依赖任何先验知识,根据数据本身的趋势特征自动提取趋势转折点,提取信息包括坐标索引和对应数据。UCR时间序列分类数据集与SEEP、CAP和PAA等算法进行对比的实验结果表明,在多种数据情况下,该算法拟合误差和分类错误率更小,平均拟合误差为0.373 6,分类错误率同原始数据的分类错误率相比减少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

CLC Number:

TP391

XING Han,SHI Xiaoda,SUN Lianying,GE Na. Trend Turning Point Extraction Algorithm for Time Series Data[J]. Computer Engineering.

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.

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URL: http://www.ecice06.com/EN/Y2018/V44/I1/56

References

参考文献［1］ESLING P,AGON C.Time-series Data Mining［J］.ACM Computing Surveys,2012,45(1):11-12. ［2］HU Zhikun,XU Fei,GUI Weihua,et al.Wavelet Matrix Transform for Time-series Similarity Measurement［J］.Journal of Central South University of Technology,2009,16(5):802-806. ［3］AGRWAL R,FALOUTSOS C,SWAMI A.Efficient Similarity Search in Sequence Databases［C］//Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms.London,UK:Springer-Verlag,1993:69-84. ［4］RAFIEI D,MENDELZON A O.Querying Time Series Data Based on Similarity［J］.IEEE Transactions on Knowledge & Data Engineering,2000,12(5):675-693. ［5］REN J M.Similarity Search over Time Series Data Using Wavelets［C］//Proceedings of the 18th International Conference on Data Engineering.San Jose,USA:IEEE Computer Society Press,2002:212-221. ［6］CHAN K,FU A.Efficient Time Series Matching by Wavelets［C］//Proceedings of the 15th IEEE International Conference on Data Engineering.Washington D.C.,USA:IEEE Press,1999:126-133. ［7］KEOGH E J,CHU S,HART D,et al.An Online Algorithm for Segmenting Time Series［C］//Proceedings of IEEE International Conference on Data Mining.Washington D.C.,USA:IEEE Press,2002:289-298. (下转第68页) (上接第61页) ［8］KEOGH E.Exact Indexing of Dynamic Time Warping［C］//Proceedings of the 28th International Conference on Very Large Databases.Hong Kong,China:［s.n.］,2002:406-417. ［9］潘定,沈钧毅.时态数据挖掘的相似性发现技术［J］.软件学报,2007,18(2):246-258. ［10］YI B K,FALOUTSOS C.Fast Time Sequence Indexing for Arbitrary LP Norms［C］//Proceedings of the 26th International Conference on Very Large Data Bases.［S.1.］:Morgan Kaufmann Publishers Inc.,2000:385-394. ［11］KEOGH E,CHAKRABARTI K,PAZZANI M,et al.Dimensionality Reduction for fast Similarity Search in Large Time Series Databases［J］.Journal of Knowledge and Information System,2001,3(3):263-286. ［12］PRAT K B,FINK E.Search for Patterns in Compressed Time Series［J］.International Journal of Image and Graphics,2002,2(1):89-106. ［13］詹艳艳,徐荣聪,陈晓云.基于斜率提取边缘点的时间序列分段线性表示方法［J］.计算机科学,2006,33(11):139-142. ［14］林意,王智博.基于一阶滤波的时间序列分段线性表示方法［J］.计算机工程,2016,42(9):151-157. ［15］周大镯,李敏强.基于序列重要点的时间序列分割［J］.计算机工程,2008,34(23):14-16. ［16］张娜,李莉娟.时间序列分段线性表示的几种算法比较［J］.中国西部科技,2009(14):80-81. ［17］CHEN Yanping,KEOGH E.UCR Time Series Classification Archive［EB/OL］.［2016-08-07］.http://www.cs.ucr.edu/~eamonn/time_series_data/. 编辑索书志

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