Survey on Feature Representation and Similarity Measurement of Time Series
QU Li SUN Dongpu · DOAJ (DOAJ: Directory of Open Access Journals) · 2021
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 achi-evements 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 non-data 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 measure-ment 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 applica-tion 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.