Time Series Classification using Compressed Recurrence Plots
In recent years recurrence plots have become a widely accepted tool for identifying and visualizing structural patterns in time series. It has been shown that the structural patterns found in recurrence plots can be used to determine the similarity between two time series, which is necessary for classification. For instance, it has been proposed to employ video compression algorithms for measuring the similarity between two recurrence plots, which visualize the structural patterns that were extracted from the time series under study. In this work we assess to what extend the choice of video compression algorithm influences the similarity measurements and classification performance for recurrence plots or time series respectively. Furthermore, we introduce a novel time series distance measure based on the compression of cross recurrence plots. Our evaluation shows that more advanced compression algorithm do not necessarily result in higher classification accuracy, but lead to superior results for relatively long time series.