# Comparing multidimensional time series with possibly non-informative dimensions

I have several examples of multidimensional time series that correspond to a particular pattern (or class) and form a dictionary. Now, based on these examples I want to find if a new time series matches to one (or all) of the patterns from the dictionary or not. For that, I am performing Dynamic Time Warping (DTW) per each dimension of every example in the dictinary with the new time series and then check if the distance between them is large or not. The problem that I have is that some of the dimensions are non informative and might be different even for time series that represent the same class. Are there any methods that help to understand which dimensions of time series are informative and which not and take into account only informative ones when performing comparison?

If I correctly understand, each of your data samples can be viewed as a matrix of [time points $\times$ features]. What you could possibly do is the following approach for each class separately: