A Novel Symbolic Representation Based on Fast Segmentation
Hong Li YIN -, Shu Yang, Ping Yin, Song Chang Jin, Hui Min Zhao · Applied Mechanics and Materials · 2014
Symbolic representation of time series has recently attracted a lot of research interest. This is a difficult problem because of the high dimensionality of the data, particularly when the length of the time series becomes longer. In this paper, we introduce a new symbolic representation based on fast segmentation, called the trend feature symbols approximation (TFSA). The experimental results show that compared to some method, the segmentation efficiency of TFSA is improved.