Genetic algorithms-based symbolic aggregate approximation

Time series data appear in a broad variety of economic, medical, and scientific applications. Because of their high dimensionality, time series data are managed by using representation methods. Symbolic representation has attracted particular attention because of the possibility it offers to benefit from algorithms and techniques of other fields in computer science. The symbolic aggregate approximation method (SAX) is one of the most important symbolic representation techniques of times series data. SAX is based on the assumption of "high Gaussianity" of normalized time series which permits it to use breakpoints obtained from Gaussian lookup tables. The use of these breakpoints is the heart of SAX. In this paper we show that this assumption of Gaussianity oversimplifies the problem and can result in very large errors in time series mining tasks. We present an alternative scheme, based on the genetic algorithms (GASAX), to find the breakpoints. The new scheme does not assume any particular distribution of the data, and it does not require normalizing the data either. We conduct experiments on different datasets and we show that the new scheme clearly outperforms the original scheme.