In this paper, we present two novel class-based

weighting methods for the Euclidean nearest neighbor algorithm

and compare them with global weighting methods

considering empirical results on a widely accepted time series

classification benchmark dataset. Our methods provide

higher accuracy than every global weighting in nearly half

of the cases and they have better overall performance. We

conclude that class-based weighting has great potential for

improving time series classification accuracy and it might be

extended to use with other distance functions than the Euclidean

distance.