Probabilistic discriminative region descriptor for time series classification

Detecting discriminivative regions is a recent promising concept in many different domains for various dataset types such as image, text and time series. In time series domain, time series might be large and high dimensional because of the developing storage capacities. Although computational capacities are improved, storage and computation costs are increased. Therefore recent attempts are focused on the decreasing the computational and run time complexities. To decrease the complexity of the models, instead of using raw data, construction of the new feature representation by using the distinctive sub-sequences of the time series is the most common approach. Discriminative sub-sequences are called as shapelets in time series reflect the characteristics of the class of time series. Shapelets provide interpretable results and shapelet-based classifiers have superior accuracy on many time series datasets. Many researchers have proposed shapelet extraction methodologies for classification purpose. This study proposes a novel local feature extraction framework for time series and shapelet-based time series classification pipeline. Proposed framework provides model selection flexibility to describe the time-observation space to find local discriminative regions. After obtaining the discriminative regions, shapelets are extracted on the time-observation space by thresholding the class probability estimates to construct a new feature representation. New feature representation is calculated by the Euclidean distance between shapelets and time series. Finally, a classifier is trained by the new feature representation. Experimental results show that shapelet-based time series classification by using proposed Probabilistic Discriminative Region Descriptor (PDRD) provides competitive results on benchmark datasets.