An improvement of SAX representation for time series by using complexity invariance
What is it about?
In the area of time series data mining, a challenging task is to design an effectively and efficiently low-dimensional representation of high-dimensional time series data. Such an effective and efficient representation is important for dimensionality reduction of time series while preserving the core information embedded in the original one. Among popular representations of time series, Symbolic Aggregate approXimation (SAX) has been widely used and is the core of many successful time se- ries data mining systems. SAX firstly normalizes the given time series, then divides a time series into segments and finally assigns each segment a symbol based on its average value. In fact, many segments have different shapes but the same average value are mapped to a sole symbol. In order to overcome this drawback, in this work, we propose an improvement of SAX by using complexity invariance, namely Complexity-invariant SAX (CSAX). In particular, our proposed method transforms a time series into a sequence of symbols based on both average values and the complexity invariance of its segments. By experiments, we demonstrate that CSAX outperforms the SAX and its improvements, i.e., ESAX, SAX_TD, SAX_SD, in time series classification.
Why is it important?
Improve the performance of time series classification