A new method for time series classification using multi-dimensional phase space and a statistical control chart

Abstract

Since large amounts of data were collected over time in many different areas, the classification of these data according to their similarities was an important problem. The methods used to classify time series are a combination of classifiers in different domains such as time, autocorrelation, frequency spectrum, and phase space. The weakest point of these methods is that they require high computational burden and the obtained features lead to misclassifications. When the phase space of the time series is modeled by the Gaussian mixture model, different conditions can be easily classified. However, this technique fails when the phase spaces of time series representing different conditions are similar. In this study, a new method for time series classification using multi-dimensional phase space is proposed using quality control charts that constructed from the phase space of a time series. It aims obtaining a new feature signal from the phase space, providing a faster method for classification of time series, and effectively detecting minor changes in time series. The method is consisted of six stages such as time series inputs, selecting an appropriate time delay and embedding dimension for each time series, construction of phase space, obtaining new time series from phase space using T2 control chart, alignment of time series with dynamic time warping, and classification with the nearest neighbor. The constructed time series is guaranteed to be a complete representation of a system where the phase space parameters are properly chosen. With the proposed new representation, the time series that belongs to different classes and whose phase spaces are similar can be easily distinguished. The k-nearest neighbor classifier is implemented for time series classification, and the datasets from two different domains are used for validation, including motor current signals and nine benchmark datasets from the UCR time series repository. The results show that the proposed method enhances the time series classification performance with new time series representation across these diverse domains.