Abstract
BackgroundEarly classification of time series is beneficial for biomedical informatics problems such including, but not limited to, disease change detection. Early classification can be of tremendous help by identifying the onset of a disease before it has time to fully take hold. In addition, extracting patterns from the original time series helps domain experts to gain insights into the classification results. This problem has been studied recently using time series segments called shapelets. In this paper, we present a method, which we call Multivariate Shapelets Detection (MSD), that allows for early and patient-specific classification of multivariate time series. The method extracts time series patterns, called multivariate shapelets, from all dimensions of the time series that distinctly manifest the target class locally. The time series were classified by searching for the earliest closest patterns.ResultsThe proposed early classification method for multivariate time series has been evaluated on eight gene expression datasets from viral infection and drug response studies in humans. In our experiments, the MSD method outperformed the baseline methods, achieving highly accurate classification by using as little as 40%-64% of the time series. The obtained results provide evidence that using conventional classification methods on short time series is not as accurate as using the proposed methods specialized for early classification.ConclusionFor the early classification task, we proposed a method called Multivariate Shapelets Detection (MSD), which extracts patterns from all dimensions of the time series. We showed that the MSD method can classify the time series early by using as little as 40%-64% of the time series’ length.
Highlights
Classification of time series is beneficial for biomedical informatics problems such including, but not limited to, disease change detection
We show the effectiveness of the Multivariate Shapelets Detection (MSD) method on a single patient from the H3N2 dataset
The MSD method used a shapelet of length 5 to classify the test subject
Summary
Classification of time series is beneficial for biomedical informatics problems such including, but not limited to, disease change detection. Extracting patterns from the original time series helps domain experts to gain insights into the classification results. This problem has been studied recently using time series segments called shapelets. The method extracts time series patterns, called multivariate shapelets, from all dimensions of the time series that distinctly manifest the target class locally. All of the aforementioned methods could be helpful in our study, and the experience of researchers and practitioners from other fields are extremely valuable, the focus of our research is to classify a new time series as early as possible by looking at and extracting patterns from past observations rather than predicting future values or analyzing a single time series’ pattern.
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