Time series classification based on multi-feature dictionary representation and ensemble learning

Time series classification is an important task for mining time series data, and many high level representations of time series have been proposed to address it. Symbolic Aggregate approXimation (SAX) is a classic high level symbolic representation method which can effectively reduce the dimensionality of time series. However, SAX-based methods for time series classification cannot achieve promising results, because SAX only extracts the mean feature of subsequence to make symbolization. In this paper, we present a novel ensemble method based on SAX called TBOPE, which is based on multi-feature dictionary representation and ensemble learning. Specifically, we first extract both the mean feature and trend feature of time series. Second, we create the histograms of two kinds of feature based on the Bag-of-Feature mode and construct multiple single classifiers. Finally, we build an ensemble classifier to improve the classification performance. Experimental results on various time series datasets have shown that the proposed method is competitive to state-of-the-art methods.

Automated summary

The study introduces a novel ensemble method called TBOPE, which is based on multi-feature dictionary representation and ensemble learning. By extracting multiple dimensions of features and constructing multiple classifiers, the method aims to improve the classification performance of time series data.