Time series clustering to improve one-class classifier performance

Abstract

The improvement of one-class classifiers’ performance through clustering of multivariate time series is considered in this paper. Datasets arising from real processes come from the available sensors and are affected by many factors, such as aging of the process, changes in the operation region, and equipment malfunction. Despite that, one expects that the classes represented by such diverse data can be unveiled via trained classifiers. This work hypothesizes that the overall performance can be improved by training sets of one-class classifiers with subsets of data clustered by similarity. The proposed method is applied to one class classifiers since they are trained only with the target class, which is clustered based on time series similarity using Dynamic Time Warping and k-means. The advantages of the techniques are illustrated through their application to a public dataset from the oil industry with instances characterizing eight classes of data represented by five time series. Seven classes are selected to train LSTM classifiers using the variables and instances clustered using time series clustering algorithms. The results show that the increase in the similarity of training data tends to improve the performance of the LSTM classifier, achieving an increase of 10% in the overall performance. In a specific case, where the clustering model raised the similarity by 84%, the classification performance improved by 21%.