  Time series classification (TSC) is the problem of learning labels from time
dependent data. One class of algorithms is derived from a bag of words
approach. A window is run along a series, the subseries is shortened and
discretised to form a word, then features are formed from the histogram of
frequency of occurrence of words. We call this type of approach to TSC
dictionary based classification. We compare four dictionary based algorithms in
the context of a wider project to update the great time series classification
bakeoff, a comparative study published in 2017. We experimentally characterise
the algorithms in terms of predictive performance, time complexity and space
complexity. We find that we can improve on the previous best in terms of
accuracy, but this comes at the cost of time and space. Alternatively, the same
performance can be achieved with far less cost. We review the relative merits
of the four algorithms before suggesting a path to possible improvement.
