  Time series classification is a field which has drawn much attention over the
past decade. A new approach for classification of time series uses
classification trees based on shapelets. A shapelet is a subsequence extracted
from one of the time series in the dataset. A disadvantage of this approach is
the time required for building the shapelet-based classification tree. The
search for the best shapelet requires examining all subsequences of all lengths
from all time series in the training set.
  A key goal of this work was to find an evaluation order of the shapelets
space which enables fast convergence to an accurate model. The comparative
analysis we conducted clearly indicates that a random evaluation order yields
the best results. Our empirical analysis of the distribution of high-quality
shapelets within the shapelets space provides insights into why randomized
shapelets sampling is superior to alternative evaluation orders.
  We present an algorithm for randomized model generation for shapelet-based
classification that converges extremely quickly to a model with surprisingly
high accuracy after evaluating only an exceedingly small fraction of the
shapelets space.
