Authors: Keiichi Tamura; Takumi Ichimura
Addresses: Graduate School of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-Higashi, Asa-Minami-Ku, Hiroshima 731-3194, Japan ' Department of Management and Systems, Prefecture University of Hiroshima, 1-1-71, Ujina-Higashi, Minami-Ku, Hiroshima 734-8559, Japan
Abstract: Time series classification is one of the most active research topics in time series data mining, because it covers a broad range of applications in many different domains. Representation for time series is a technique that converts time series to feature vectors representing the characteristics of time series. The performance of classifying time series depends on this representation. Chaotic time series analyses have been well-studied. Moreover, recurrence plotting underlying chaos theory is one of the most robust representation for time series. In this study, we propose a new time series representation utilising the recurrence plot technique. Moving average convergence divergence (MACD) histogram is the acceleration of time that can represent local-variation in time series. Therefore, a recurrence plot that is made from MACD histogram, which is called a MACD-histogram-based recurrence plot (MHRP), can handle time series very well. Recurrence plots are referred to as grey-scale images and we utilise stacked auto-encoders as a classifier for MHRPs. To evaluate the performance of the proposed classifier, experiments using the UCR time series classification archive was conducted. The experimental results showed that the proposed classifier outperforms other methods.
Keywords: time series classification; time series mining; recurrence plot; chaotic time series analysis; MACD histogram.
International Journal of Computational Intelligence Studies, 2018 Vol.7 No.3/4, pp.192 - 213
Received: 06 Feb 2018
Accepted: 07 May 2018
Published online: 13 Nov 2018 *