  The matrix profile (MP) is a data structure computed from a time series which
encodes the data required to locate motifs and discords, corresponding to
recurring patterns and outliers respectively. When the time series contains
noisy data then the conventional approach is to pre-filter it in order to
remove noise but this cannot apply in unsupervised settings where patterns and
outliers are not annotated. The resilience of the algorithm used to generate
the MP when faced with noisy data remains unknown. We measure the similarities
between the MP from original time series data with MPs generated from the same
data with noisy data added under a range of parameter settings including adding
duplicates and adding irrelevant data. We use three real world data sets drawn
from diverse domains for these experiments Based on dissimilarities between the
MPs, our results suggest that MP generation is resilient to a small amount of
noise being introduced into the data but as the amount of noise increases this
resilience disappears
