Early classification of time series by simultaneously optimizing the accuracy and earliness
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The problem of early classi cation of time series appears naturally in contexts where the data, of temporal nature, is collected over time, and early class predictions are interesting or even required. The objective is to classify the incoming sequence as soon as possible, while maintaining suitable levels of accuracy in the predictions. Thus, we can say that the problem of early classi cation consists in optimizing two objectives simultaneously: accuracy and earliness. In this context, we present a method for early classi cation of time series based on combining a set of probabilistic classi ers together with a stopping rule. This stopping rule will act as a trigger and will tell us when to output a prediction or when to wait for more data, and it's main novelty lies in the fact that it is built by explicitly optimizing a cost function based on accuracy and earliness. We have selected a large set of benchmark datasets and 4 other state-of- the-art early classi cation methods and we have evaluated and compared our framework obtaining superior results in terms of both earliness and accuracy.