Time series clustering using stochastic and deterministic influences
Mirlei Moura Da Silva, Rodrigo Fernandes de Mello, Ricardo A. Rios · International Journal of Computational Science and Engineering · 2020
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.