Early Prediction on Time Series: A Nearest Neighbor Approach
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. Zhengzheng Xing, Jian Pei, Philip S. Yu
Cite
Text
Xing et al. "Early Prediction on Time Series: A Nearest Neighbor Approach." International Joint Conference on Artificial Intelligence, 2009.Markdown
[Xing et al. "Early Prediction on Time Series: A Nearest Neighbor Approach." International Joint Conference on Artificial Intelligence, 2009.](https://mlanthology.org/ijcai/2009/xing2009ijcai-early/)BibTeX
@inproceedings{xing2009ijcai-early,
title = {{Early Prediction on Time Series: A Nearest Neighbor Approach}},
author = {Xing, Zhengzheng and Pei, Jian and Yu, Philip S.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2009},
pages = {1297-1302},
url = {https://mlanthology.org/ijcai/2009/xing2009ijcai-early/}
}