Time Warping Symbolic Aggregation Approximation with Bag-of-Patterns Representation for Time Series Classification

Standard Symbolic Aggregation approXimation (SAX) is at the core of many effective time series data mining algorithms. Its combination with Bag-of-Patterns (BoP) has become the standard approach with state-of-the-art performance on standard datasets. However, standard SAX with the BoP representation might neglect internal temporal correlation embedded in the raw data. In this paper, we proposed time warping SAX, which extends the standard SAX with time delay embedding vector approaches to account for temporal correlations. We test time warping SAX with the BoP representation on 12 benchmark datasets from the UCR Time Series Classification/Clustering Collection. On 9 datasets, time warping SAX overtakes the state-of-the-art performance of the standard SAX. To validate our methods in real world applications, a new dataset of vital signs data collected from patients who may require blood transfusion in the next 6 hours was tested. All the results demonstrate that, by considering the temporal internal correlation, time warping SAX combined with BoP improves classification performance.