A hybrid method for novelty detection in time series based on states transitions and swarm intelligence

This paper introduces a novel instance-based one-class classification method for novelty detection in time series based on its states transition. The main feature of our work is to generate an efficient method which automatically finds the parameters (whose yields the best model) according with the quality of the discovered time series states and the validation error. This method involves clustering and reducing the number of samples in a training dataset which does not contain novelty samples. Experiments carried out using three real-world time series show that the proposed method is able to build models with a reduced number of stored prototypes. The results obtained by our method were compared with the results of the SAX and both methods have successfully detected the novelties, however, the parameters which resulted in the best SAX model were achieved without validation phase (i.e. analyzing the results obtained for the test set).