COSTI: a New Classifier for Sequences of Temporal Intervals
This addresses a domain-specific problem in time series analysis for event sequence classification, with incremental improvements over existing methods.
The authors tackled the problem of classifying sequences of temporal intervals by proposing COSTI, a method that operates directly on such sequences, achieving significantly better accuracy than state-of-the-art methods and introducing a generalized version with intensity information.
Classification of sequences of temporal intervals is a part of time series analysis which concerns series of events. We propose a new method of transforming the problem to a task of multivariate series classification. We use one of the state-of-the-art algorithms from the latter domain on the new representation to obtain significantly better accuracy than the state-of-the-art methods from the former field. We discuss limitations of this workflow and address them by developing a novel method for classification termed COSTI (short for Classification of Sequences of Temporal Intervals) operating directly on sequences of temporal intervals. The proposed method remains at a high level of accuracy and obtains better performance while avoiding shortcomings connected to operating on transformed data. We propose a generalized version of the problem of classification of temporal intervals, where each event is supplemented with information about its intensity. We also provide two new data sets where this information is of substantial value.