Generalized Minkowski Distancemeasure the similarity or dissimilarity between two Time series by computing the sum of the absolute differencesd(x,y)=∑i=1d(xi−yi)pp d(x,y) = \sqrt[p]{\sum_{i=1}^{d} (x_i - y_i)^p}d(x,y)=p∑i=1d(xi−yi)pWhen p is 2Euclidean Distance When p is 1Manhattan Distance When p is ∞\infty∞Infinity Distance d(x,y)=maxi=1d∣xi−yi∣d(x,y) = \max_{i=1}^d |x_i - y_i|d(x,y)=maxi=1d∣xi−yi∣ Minkowski distanceThe Minkowski distance or Minkowski metric is a metric in a normed vector space which can be considered as a generalization of both the Euclidean distance and the Manhattan distance. It is named after the Polish mathematician Hermann Minkowski.https://en.wikipedia.org/wiki/Minkowski_distance