  A family of algorithms for time series classification (TSC) involve running a
sliding window across each series, discretising the window to form a word,
forming a histogram of word counts over the dictionary, then constructing a
classifier on the histograms. A recent evaluation of two of this type of
algorithm, Bag of Patterns (BOP) and Bag of Symbolic Fourier Approximation
Symbols (BOSS) found a significant difference in accuracy between these
seemingly similar algorithms. We investigate this phenomenon by deconstructing
the classifiers and measuring the relative importance of the four key
components between BOP and BOSS. We find that whilst ensembling is a key
component for both algorithms, the effect of the other components is mixed and
more complex. We conclude that BOSS represents the state of the art for
dictionary based TSC. Both BOP and BOSS can be classed as bag of words
approaches. These are particularly popular in Computer Vision for tasks such as
image classification. Converting approaches from vision requires careful
engineering. We adapt three techniques used in Computer Vision for TSC: Scale
Invariant Feature Transform; Spatial Pyramids; and Histrogram Intersection. We
find that using Spatial Pyramids in conjunction with BOSS (SP) produces a
significantly more accurate classifier. SP is significantly more accurate than
standard benchmarks and the original BOSS algorithm. It is not significantly
worse than the best shapelet based approach, and is only outperformed by
HIVE-COTE, an ensemble that includes BOSS as a constituent module.
