André Gustavo Maletzke

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In recent years many studies have been proposed for knowledge discovery in time series. Most methods use some technique to transform raw data into another representation. Symbolic representations approaches have shown effectiveness in speedup processing and noise removal. The current most commonly used algorithm is the Symbolic Aggregate Approximation(More)
In the last decade symbolic representations approaches have been proposed for knowledge discovery in time series. However, the conventional symbolic methods ignore the temporal order of symbols, so this core feature of time series is lost. In this paper, to treat this problem we present a symbolic representation method to incorporate the temporal(More)
In the last decade, it has increased the interest of researchers and professionals from various areas in the analysis of data that have a temporal dependency, aiming to identify patterns and relationships that might exist in data over the time. An approach recently introduced in temporal data mining is the identification of frequently occurring patterns,(More)
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