时间序列特征表示与相似性度量研究综述

时间序列是将同一指标的数值按照时间的先后顺序排列组成的一组随机数列.随着科学技术的蓬勃发展,时间序列在数据挖掘领域中的应用变得越来越广泛.综合分析了近年来时间序列在数据挖掘领域的文献成果,对时间序列特征表示和相似性度量方法进行了阐述.针对时间序列特征表示方法,从非数据适应性方法、数据自适应性方法、基于模型的方法三方面进...

Survey on Feature Representation and Similarity Measurement of Time Series

Time series is a group of random numbers which are composed of the values of the same index according to the time sequence.With the rapid development of science and technology,the application of time series in the field of data mining becomes more and more extensively.This paper comprehensively analyzes the literature achievements of time series in the field of data mining in recent years,and expounds the methods of time series in feature representation and similarity measurement.For the feature representation methods of time series,the nondata adaptive methods,data self-adaptive methods and model-based methods are introduced.The research status,advantages and disadvantages,application fields,method characteristics and limitations of various main methods are compared and analyzed.For the similarity measurement methods of time series,the shape-based similarity measurement methods,model-based similarity measurement methods and data-compression-based similarity measurement methods are described systematically.The advantages and disadvantages of various main methods and their application fields are introduced.Some characteristics of different aspects are also compared and analyzed,such as whether to support the comparison between unequal length time series,whether to support translation,and whether to support trigonometric inequality.Finally,the future research direction of time series is prospected.