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Proceedings of the 10th IEEE International Workshop on Computational Intelligence and Applications (IWCIA 2017), 135–140p. (2017) DOI:10.1109/IWCIA.2017.8203574
Time series classification is the most active research topics in time series data mining, because they cover a broad range of applications in many different domains. There are three important things that we need to consider in time series classification; representation, similarity measurement, and assignment strategy. Representation for time series is a technique that converts time series to feature vectors representing the characteristics of time series. Chaotic time series analysis have been well-studied; Moreover, recurrence plotting underlying chaos theory is one of the most robust the feature expression for time series. In this study, we propose new representation for the time series classification utilizing the recurrence plot technique. Moving average convergence divergence (MACD) histogram is the acceleration of time that represents the features of time series. Therefore, the proposed method is based on MACD histogram. In particular, a recurrence plot that is made from MACD histogram is called a MACD-Histogram-based recurrence plot (MHRP). The recurrence plot is referred to as a gray-scale image and we utilize stacked auto-encoders as a classier for MHRPs. To evaluate the performance of the classifier with the MHRP technique, we implemented it and conducted experiments using the UCR time series classification archive. The experimental results showed that the proposed classifier outperforms not only distance-based 1-NNs, but also our previous MACD-Histogram-based method.
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