  Using bag of words representations of time series is a popular approach to
time series classification. These algorithms involve approximating and
discretising windows over a series to form words, then forming a count of words
over a given dictionary. Classifiers are constructed on the resulting
histograms of word counts. A 2017 evaluation of a range of time series
classifiers found the bag of symbolic-fourier approximation symbols (BOSS)
ensemble the best of the dictionary based classifiers. It forms one of the
components of hierarchical vote collective of transformation-based ensembles
(HIVE-COTE), which represents the current state of the art. Since then, several
new dictionary based algorithms have been proposed that are more accurate or
more scalable (or both) than BOSS. We propose a further extension of these
dictionary based classifiers that combines the best elements of the others
combined with a novel approach to constructing ensemble members based on an
adaptive Gaussian process model of the parameter space. We demonstrate that the
temporal dictionary ensemble (TDE) is more accurate than other dictionary based
approaches. Furthermore, unlike the other classifiers, if we replace BOSS in
HIVE-COTE with TDE, HIVE-COTE is significantly more accurate. We also show this
new version of HIVE-COTE is significantly more accurate than the current best
deep learning approach, a recently proposed hybrid tree ensemble and a recently
introduced competitive classifier making use of highly randomised convolutional
kernels. This advance represents a new state of the art for time series
classification.
