Comparing Euclidean distances with dynamic time warping (DTW): Will Euclidean distance perform better than DTW when clustering time series that all have the same length and sampling interval? Are there arguments in favor of DTW for this special case?
Solved – Does it make sense to use dynamic time warping when clustering time series that all have the same length and sampling interval
time series
Best Answer
It depends on what you mean by performance - in terms of computing power DTW suffers from computational lethargy and Euclidean distance method dosnt. DTW is superior when it comes to classification and clustering.
Euclidean distance, which assumes the ith point in one sequence is aligned with the ith point in the other, will produce a pessimistic dissimilarity measure. The non-linear Dynamic Time Warped alignment allows a more intuitive distance measure to be calculated.
see Making Time-series Classification More Accurate Using Learned Constraints
I have used both DTW and Euclidean
Happy clustering !!!!