Symbolic Time Series Representation for Stream Data Processing

Over the past years, many representations for time series were proposed with the main purpose of dimensionality reduction and as a support for various algorithms in the domain of time series data processing. However, most of the transformation algorithms are not directly applicable on streams of data but only on static collections of the data as they are iterative in their nature. In this work we propose a symbolic representation of time series along with the method for transformation of the data into proposed representation. As one of the basic requirements for applicable representation is the distance measure which would accurately reflect the true shape of the data, we propose a distance measure operating on the proposed representation and lower bounding the Euclidean distance on the original data. We evaluate properties of the proposed representation and the distance measure on a number of different datasets.