Stationary time series for clustering algorithms I have a set of time series data that I would like to feed into a clustering algorithm (like k-means, using dynamic time warping as the distance function). After standardizing the data with mean 0 and variance 1, the k-means classifier generated a batch of centroids that seemed to fit the data pretty well.

The only question I have is whether the data should be stationary. Models such as ARIMA require for the data to be stationary due to the nature of it. However, the data I want to cluster is mortgage rates as a function of time, which could be subject to seasonal trends, which could be useful when clustering other future time series data.

The question is: do clustering algorithms for time series data generally require for the data to be stationary?
Have you tried replacing the entire timeseries with some meta data of it - clustering on each point of a series seems to be a bit exzessive. Especially as then if your timeseries are not synchronized they might not be considered similar - eventhough they might be for the purpose of this exercise (Imagine two sinus curves but one is slightly phase shifted -- If you would want to cluster by frequency then these would be equal). 

So you could try to do something similar to Fourier transformations Fourier Transformations, linear fits, etc and use this meta data as input for the clustering.