COSTI: a New Classifier for Sequences of Temporal Intervals

TL;DR

This paper introduces COSTI, a novel classification method for sequences of temporal intervals that directly operates on raw data, improving accuracy and addressing limitations of existing transformation-based approaches.

Contribution

The paper presents COSTI, a new direct classification method for temporal intervals, and extends the problem to include event intensity, supported by new datasets.

Findings

COSTI outperforms state-of-the-art transformation-based methods in accuracy.

The method effectively handles sequences with additional intensity information.

New datasets demonstrate the importance of intensity in classification tasks.

Abstract

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.