I'm working on the same type of data and i want to classify the times series to find clear pattern of use. My data is collected from clients of a telecom company, and we want to detect pattern of the amount of data consumed by clients with their wifi box. So each client have a time series of how much data he consumed each 6minutes (I resampled it to hours). I also applied DTW with KmeansTimeSeries using
km = TimeSeriesKMeans(n_clusters = cluster_count, metric="dtw", verbose=1)
labels = km.fit_predict(mySeries)
My question is i want to change the warping window for DTW in python, and i'm pretty sure it's easy think to do, but i just coudn't find a way to do it. I also want to find the best window parameter for my case.