The characterization of a measure of information discrepancy
Information Sciences—Applications: An International Journal
A measure of discrepancy of multiple sequences
Information Sciences: an International Journal
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
A clustering procedure for exploratory mining of vector time series
Pattern Recognition
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Clustering of time series data-a survey
Pattern Recognition
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Similarity measure is a basic task in time series data mining and attracts much attention in the last decade. This paper considers time series similarity measure from an information theoretic perspective. Based on the function of degree of disagreement (FDOD), a new time series similarity measure method is proposed. The empirical result indicates that the method of this paper can solve the unequal time series and has less time complexity. Meanwhile, it also can measure the similarity between multivariate time series.