document: Optimizing time series discretization for knowledge discovery Knowledge Discovery in time series usually requires symbolic time series . Many discretization methods that convert numeric time series to symbolic time series ignore the temporal order of values . This often leads to symbols that do not correspond to states of the process generating the time series and can not be interpreted meaningfully . We propose a new method for meaningful unsupervised discretization of numeric time series called Persist . The algorithm is based on the Kullback-Leibler divergence between the marginal and the self-transition probability distributions of the discretization symbols . Its performance is evaluated on both artificial and real life data in comparison to the most common discretization methods . Persist achieves significantly higher accuracy than existing static methods and is robust against noise . It also outperforms Hidden Markov Models for all but very simple cases .
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