a8849b052492b5106526b2331e526138-Reviews.html

The authors examine theoretically and empirically properties of the k 1-nearest neighbor classification (NN) and a trivial variant of the kernel density classification, called "weighted majority voting", in a time-series binary classification problem. For theoretical analysis, a simplified generative model for time-series is introduced. Under this model, they provide non-asymptotic performance guarantees in terms of how large of a training dataset and how much of the time-series length to be classified. Although the theoretical analysis is technically sound, the empirical work will be incomplete. In the experiments, the weighted majority voting and the NN are just tested while data analysts would at least use the k-NN, rather than the NN, with a tuned'k' by cross-validation or so.