This package provides Pythonic (>=2.6) implementation of Mueen-Keogh (MK) algorithm for finding motifs in the time series.
Mueen, A., Keogh, E., Zhu, Q., Cash, S. and Westover, B. Exact Discovery of Time Series Motif. SDM 2009 (link).
This algorithm aims to find significant pairs of motifs (identical subsequences in a time-series which repeat more than once) in a given time-series. The usual bruteforce methods of finding these motifis are usually O(n^2) in terms of their complexity, while MK Algorithm aims to find them in O(n * log(n)) order.
This implementation contains both MK-Early Abandoning and standard MK algorithm implementations:
>>> from mkalgo.mk import mk_eab, mk>>> obj = mk_eab(l=5, metric='euclidean')
>>> motif_a, motif_b = obj.search(timeseries)>>> obj = mk_eab(l=5, metric='euclidean', r=10)
>>> motif_a, motif_b = obj.search(timeseries)lrefers to the desired length of motif pairs to be foundmetricrefers to the distance metric to be utilized, which can beeuclideanordtw(dynamic time warping).
The returned object is a dictionary, containing the respective motif and its beginning and ending indices in the given timeseries.
Both the implementations can be used in the similar fashion, with mk taking an additional parameter of r, which refers to the number of rounds made over n/l chunks of the time-series to find the significant reference point.