Feature-based Classi cation of Time-series Data ALEX NANOPOULOS ROB ALCOCK

In this paper we propose the use of statistical features for time-series classi cation. The classi cation is performed with a multi-layer perceptron (MLP) neural network. The proposed method is examined in the context of Control Chart Pattern data, which are time series used in Statistical Process Control. Experimental results verify the e ciency of the feature-based classi cation method, compared to previous methods which classify time series based on the values of each time point. Moreover, the results show the robustness of the proposed method against noise and time-series length. Key-words: Data mining, time series, classi cation, statistical features