8. Bag of words for time series

Several algorithms for time series classification are based on bag-of-words approaches: a sequence of symbols is transformed into a bag of words. Utilities to derive bag of words are provided in the pyts.bag_of_words module.

8.1. Bag of words

BagOfWords extracts words from a sequence of symbols. This sequence of symbols is usually a discretized time series or discretized Fourier coefficients of a time series. Words are extracted using a sliding window that can be controlled with the window_size and window_step parameters. The numerosity_reduction parameter controls the removal of all but one occurrence of identical consecutive words. The impact of this parameter is illustrated in the following example: when a time series has low variation over several time points, the discretized time series is constant, which leads to several identical consecutive words. Removed words are almost transparent.

../_images/sphx_glr_plot_bow_0011.png 
>>> from pyts.bag_of_words import BagOfWords
>>> X = [['a', 'a', 'b', 'a', 'b', 'b', 'b', 'b', 'a'],
...      ['a', 'b', 'c', 'c', 'c', 'c', 'a', 'a', 'c']]
>>> bow = BagOfWords(window_size=2)
>>> print(bow.transform(X))
['aa ab ba ab bb ba' 'ab bc cc ca aa ac']
>>> bow = BagOfWords(window_size=2, numerosity_reduction=False)
>>> print(bow.transform(X))
['aa ab ba ab bb bb bb ba' 'ab bc cc cc cc ca aa ac']