Solved – What are some fast alternatives to dynamic time warping?

I am planning to cluster a great amount of time series of different lengths into groups without using the method Dynamic Time Warping, but something else which gives a little better execution time and results. What's your opinion?

I mean that I need an algorithm, exept from DTW but a litter better than it, that measures similarity between temporal sequences -times series of different lengths- and then performs clustering using distance measures techniques as criterion. Then I will be able to perform forecasting to time series according to the train sets time series which I have clustered

Better results that DTW? For the problems of time series classification, there is nothing significantly better than DTW, see

As for "better execution time", DTW with a warping constraint and LB_keogh is fast enough for virtually any task.


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