Time series clustering using stochastic and deterministic influences
by Mirlei Moura Da Silva; Rodrigo Fernandes De Mello; Ricardo Araújo Rios
International Journal of Computational Science and Engineering (IJCSE), Vol. 21, No. 3, 2020

Abstract: Time series clustering aims at designing methods to extract patterns from temporal data in order to organise series according to their similarities. In general, most of researches either perform a preprocessing step to convert time series into attribute-value matrices to be later analysed by traditional clustering methods, or apply measures specifically designed to compute the similarity among time series. We noticed two main issues in such studies: 1) clustering methods do not take into account stochastic and deterministic influences inherent in real-world time series; 2) similarity measures tend to look for recurrent patterns, which may not be available in stochastic time series. In order to overcome such drawbacks, we present a new clustering approach that considers both influences and a new similarity measure to deal with purely stochastic time series. Experiments provided outstanding results, emphasising time series are better clustered when their stochastic and deterministic influences are properly analysed.

Online publication date: Fri, 27-Mar-2020

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