Abstract: This paper presents two approaches for using Process Mining to the analysis of time series. They are applied to a time series of Meteosat full-disk water-vapor satellite images characterized by their pairwise similarity. Event logs constructed from this time series are processed with the Inductive Miner algorithm producing models in the form of Petri Nets and Directly-Follows Graphs (DFG). In the first approach, logs corresponding to five years of similarity indexes of daily images were grouped by astronomical seasons and Petri Nets models were...
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Topics: 
Data mining
Artificial intelligence
Machine learning