A multi-breakpoints approach for symbolic discretization of time series

Time series discretization is a technique commonly used to tackle time series classification problems. This manuscript presents an enhanced multi-objective approach for the symbolic discretization of time series called eMODiTS. The method proposed uses a different breakpoints vector, defined per each word segment, to increase the search space of the discretization schemes. eMODiTS’ search mechanism is the well-known evolutionary multi-objective algorithm NSGA-II, which finds a set of possible solutions according to entropy, complexity, and information loss estimations. Final solutions were appraised depending on the misclassification rate computed through the decision tree classifier. The trees obtained also produce graphical and significant information from the regions, relationships, or patterns in each database. Our proposal was compared against ten state-of-the-art time symbolic discretization algorithms. The results suggest that our proposal finds a suitable discretization scheme regarding classification, dimensionality, cardinality reduction, and information loss.