Symbolic Aggregate Approximation Based on Shape Features
Hailin Li · 2011
Changeable trends of time series can be reflected by shape features which retain sufficient data information during the dimensionality reduction.It is good to improve the efficiency of time series data mining in the later stage.A symbolic aggregate approximation based on shape features is proposed.It regards the mean and the shape feature of a sequence as two important characteristics,and changes their domains of discourse to transform them into strings.Compared with the traditional methods,the proposed method improves the efficiency of time series data mining in the setting of equal compress rate because of the sufficient information which is retained by the previous stage.