  Time series shapelets are discriminative subsequences and their similarity to
a time series can be used for time series classification. Since the discovery
of time series shapelets is costly in terms of time, the applicability on long
or multivariate time series is difficult. In this work we propose Ultra-Fast
Shapelets that uses a number of random shapelets. It is shown that Ultra-Fast
Shapelets yield the same prediction quality as current state-of-the-art
shapelet-based time series classifiers that carefully select the shapelets by
being by up to three orders of magnitudes. Since this method allows a
ultra-fast shapelet discovery, using shapelets for long multivariate time
series classification becomes feasible.
  A method for using shapelets for multivariate time series is proposed and
Ultra-Fast Shapelets is proven to be successful in comparison to
state-of-the-art multivariate time series classifiers on 15 multivariate time
series datasets from various domains. Finally, time series derivatives that
have proven to be useful for other time series classifiers are investigated for
the shapelet-based classifiers. It is shown that they have a positive impact
and that they are easy to integrate with a simple preprocessing step, without
the need of adapting the shapelet discovery algorithm.
