[ENH] Parameterise the dtw and adtw distances

[itsdivya1309]

Describe the feature or idea you want to propose

Recent advances in similarity-based classification, like the Proximity Forest 2.0 include parameterising cost functions used in similarity measures. However, the current aeon implementation of dtw uses squared distance as the cost function. The Minkowski distance is a generalised form of both the Euclidean and Manhattan distance, and hence can be used as the cost function for dtw and adtw.

Describe your proposed solution

Use Minkowski distance instead of squared_univariate_distance to compute the dtw cost matrix.
distance = np.sum((np.abs(x - y) ** p)) ** (1.0 / p)

Describe alternatives you've considered, if relevant

No response

Additional context

No response