A Boundary Distance Based Symbolic Aggregate Approximation Method for Time Series Data
A large amount of time series data is being generated every day in a wide range of sensor application domains. The symbolic aggregate approximation (SAX) is a well known time series representation method, which has a lower bound to Euclidean distance and may discretize continuous time series. SAX has been widely used for applications in various domains, such as mobile data management, financial investment, and shape discovery. However, the SAX representation has a limitation: Symbols are mapped from the average values of segments, but SAX does not consider the boundary distance in the segments. Different segments with similar average values may be mapped to the same symbols, and the SAX distance between them is 0. In this paper, we propose a novel representation named SAX BD (boundary distance) by integrating the SAX distance with a weighted boundary distance. The experimental results show that SAX BD significantly outperforms the SAX representation, ESAX representation, and SAX TD representation.
Algorithms