Time series classification (TSC) is becoming very important in the area of pattern recognition with the increased availability of time series data in various natural and real life phenomena. TSC is a challenging problem because, due to the attributes being ordered, traditional machine learning algorithms for static data are not quite suitable for processing temporal data. Due to the gradual increase of computing power, a large number of TSC algorithms have been developed recently. In addition to traditional feature-based, model-based or distance-based algorithms, ensemble and deep networks have recently become popular for time series classification. Time series are essentially huge, and classifying raw data is computationally expensive in terms of both processing and storage. Representation techniques for data reduction and ease of visualization are needed for accurate classification. In this work a recurrence plot-based data representation is proposed and time series classification in conjunction with a deep neural network-based classifier has been studied. A simulation experiment with 85 benchmark data sets from UCR repository has been undertaken with several state of the art algorithms for time series classification in addition to our proposed scheme of classification for comparative study. It was found that, among non-ensemble algorithms, the proposed algorithm produces the highest classification accuracy for most of the data sets.
Effect of Data Representation for Time Series Classification—A Comparative Study and a New Proposal
