In this paper, we formulate the problem of early classification of time series data, which is important in some time-sensitive applications such as healthinformatics. We introduce a novel concept of MPL (Minimum Prediction Length) and develop ECTS (Early Classification on Time Series), an effective 1-nearest neighbor classification method. ECTS makes early predictions and at the same time retains the accuracy comparable to that of a 1NN classifier using the full-length time series. Our empirical study using benchmark time series data sets shows that ECTS works well on the real data sets where 1NN classification is effective.

Authors

• Zhengzheng Xing
• Jian Pei School of Computing Science, Simon Fraser University
• Philip S. Yu University of Illinois, Chicago

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