  Time series (TS) occur in many scientific and commercial applications,
ranging from earth surveillance to industry automation to the smart grids. An
important type of TS analysis is classification, which can, for instance,
improve energy load forecasting in smart grids by detecting the types of
electronic devices based on their energy consumption profiles recorded by
automatic sensors. Such sensor-driven applications are very often characterized
by (a) very long TS and (b) very large TS datasets needing classification.
However, current methods to time series classification (TSC) cannot cope with
such data volumes at acceptable accuracy; they are either scalable but offer
only inferior classification quality, or they achieve state-of-the-art
classification quality but cannot scale to large data volumes.
  In this paper, we present WEASEL (Word ExtrAction for time SEries
cLassification), a novel TSC method which is both scalable and accurate. Like
other state-of-the-art TSC methods, WEASEL transforms time series into feature
vectors, using a sliding-window approach, which are then analyzed through a
machine learning classifier. The novelty of WEASEL lies in its specific method
for deriving features, resulting in a much smaller yet much more discriminative
feature set. On the popular UCR benchmark of 85 TS datasets, WEASEL is more
accurate than the best current non-ensemble algorithms at orders-of-magnitude
lower classification and training times, and it is almost as accurate as
ensemble classifiers, whose computational complexity makes them inapplicable
even for mid-size datasets. The outstanding robustness of WEASEL is also
confirmed by experiments on two real smart grid datasets, where it
out-of-the-box achieves almost the same accuracy as highly tuned,
domain-specific methods.
