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Early prediction on time series: A nearest neighbor approach

Publication ,  Conference
Xing, Z; Pei, J; Yu, PS
Published in: IJCAI International Joint Conference on Artificial Intelligence
January 1, 2009

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.

Duke Scholars

Published In

IJCAI International Joint Conference on Artificial Intelligence

ISSN

1045-0823

ISBN

9781577354260

Publication Date

January 1, 2009

Start / End Page

1297 / 1302
 

Citation

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Xing, Z., Pei, J., & Yu, P. S. (2009). Early prediction on time series: A nearest neighbor approach. In IJCAI International Joint Conference on Artificial Intelligence (pp. 1297–1302).
Xing, Z., J. Pei, and P. S. Yu. “Early prediction on time series: A nearest neighbor approach.” In IJCAI International Joint Conference on Artificial Intelligence, 1297–1302, 2009.
Xing Z, Pei J, Yu PS. Early prediction on time series: A nearest neighbor approach. In: IJCAI International Joint Conference on Artificial Intelligence. 2009. p. 1297–302.
Xing, Z., et al. “Early prediction on time series: A nearest neighbor approach.” IJCAI International Joint Conference on Artificial Intelligence, 2009, pp. 1297–302.
Xing Z, Pei J, Yu PS. Early prediction on time series: A nearest neighbor approach. IJCAI International Joint Conference on Artificial Intelligence. 2009. p. 1297–1302.

Published In

IJCAI International Joint Conference on Artificial Intelligence

ISSN

1045-0823

ISBN

9781577354260

Publication Date

January 1, 2009

Start / End Page

1297 / 1302