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Early Prediction on Time Series: A Nearest Neighbor Approach

2019-11-15 | |  86 |   41 |   0

Abstract In this paper, we formulate the problem of early classifification 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 Classifification on Time Series), an effective 1-nearest neighbor classifification method. ECTS makes early predictions and at the same time retains the accuracy comparable to that of a 1NN classififier 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 classifification is effective

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