Time series data mining with exact primitives

Data mining and knowledge discovery algorithms for time series data use primitives such as bursts, periods, motifs, outliers and shapelets as building blocks. For example a model of global temperature considers both bursts (i.e. solar fare) and periods (i.e. sunspot cycle) of the sun. Algorithms for finding these primitives are required to be fast to process large datasets. Because exact algorithms that guarantee the optimum solutions are very slow for their immense computational requirements, existing algorithms find primitives approximately.

This thesis presents efficient exact algorithms for two primitives, time series motif and time series shapelet. A time series motif is any repeating segment whose appearances in the time series are too similar to happen at random and thus expected to bear important

information about the structure of the data. A time series shapelet is any subsequence that describes a class of time series differentiating from other classes and thus can be used to classify unknown instances. We extend the primitives for different environments