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Classification of sequences of temporal intervals is a part of time seriesanalysis which concerns series of events. We propose a new method oftransforming the problem to a task of multivariate series classification. Weuse one of the state-of-the-art algorithms from the latter domain on the newrepresentation to obtain significantly better accuracy than thestate-of-the-art methods from the former field. We discuss limitations of thisworkflow and address them by developing a novel method for classificationtermed COSTI (short for Classification of Sequences of Temporal Intervals)operating directly on sequences of temporal intervals. The proposed methodremains at a high level of accuracy and obtains better performance whileavoiding shortcomings connected to operating on transformed data. We propose ageneralized version of the problem of classification of temporal intervals,where each event is supplemented with information about its intensity. We alsoprovide two new data sets where this information is of substantial value.

COSTI: a New Classifier for Sequences of Temporal Intervals