Similarity¶

similarity.find_neighbors(query, ts, w, min_dist, exclusion_zone=None, max_neighbors=100, radius=3)[source]¶

Given a query of length w, search for similar patterns in the timeseries ts. Patterns with a distance less than (radius * min_dist): from the query are considered similar(neighbors). This function supports multi-dimensional queries and time series. The distance is calculated based on the multi-dimensional distance profile as described at (https://www.cs.ucr.edu/~eamonn/Motif_Discovery_ICDM.pdf). This function is implemented based on the univariate apporaches ofthe matrix profile foundation library.

Parameters

query – The query that will be compared against the time series. Can be univariate or multi-dimensional.
ts – A time series. Can be univariate or multi-dimensional.
w – The subsequence window size (should be the length of the query).
min_dist – The minimum distance that will be multiplied with radius to compute maximum distance allowed for a subsequence to be considered similar.
exclusion_zone – The exclusion zone to use.
max_neighbors – The maximum amount of neighbors to find for each of the top k motifs.
radius – The radius to multiply min_dist with in order to create the maximum distance allowed for a subsequence to be considered similar.

similarity.get_top_k_motifs(df, mp, index, m, ez, radius, k, max_neighbors=50)[source]¶

Given a matrix profile, a matrix profile index, the window size and the DataFrame that contains a multi-dimensional timeseries,: Find the top k motifs in the timeseries, as well as neighbors that are within the range <radius * min_mp_value> of each of the top k motifs. Uses an extended version of the top_k_motifs function from matrixprofile foundation library that is compatible with multi-dimensional timeseries. The implementation can be found here (https://github.com/MORE-EU/matrixprofile/blob/master/matrixprofile/algorithms/top_k_motifs.py)

Parameters

df – DataFrame that contains the multi-dimensional timeseries that was used to calculate the matrix profile.
mp – A multi-dimensional matrix profile.
index – The matrix profile index that accompanies the matrix profile.
m – The subsequence window size.
ez – The exclusion zone to use.
radius – The radius to use.
k – The number of the top motifs that were found.
max_neighbors – The maximum amount of neighbors to find for each of the top k motifs.

similarity.pairwise_dist(q1, q2)[source]¶

Calculates the distance between two time series sequences q1, q2. The distance is calculated based on the multi-dimensional distance profile.: This function allows for the comparison of univariate and multi-dimensional sequences.

Parameters