AAAI Publications, Twenty-First International Joint Conference on Artificial Intelligence

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Early Prediction on Time Series: A Nearest Neighbor Approach
Zhengzheng Xing, Jian Pei, Philip S. Yu

Last modified: 2009-06-26


In this paper, we formulate the problem of early classification of time series data, which is important in some time-sensitive applications such as health-informatics. 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.

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