Early prediction on time series: a nearest neighbor approach

Authors:
Zhengzheng Xing;Jian Pei;Philip S. Yu
Affiliations:
School of Computing Science, Simon Fraser University;School of Computing Science, Simon Fraser University;Department of Computer Science, University of Illinois at Chicago
Venue:
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Year:
2009

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Abstract

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.