Subsequence search is to match the best occurance of a short time serise in a longer series.
DTW subsequence alignment¶
Given a series:
And a query:
We can find the best occurence(s) as follows:
from dtaidistance.subsequence.dtw import subsequence_alignment from dtaidistance import dtw_visualisation as dtwvis sa = subsequence_alignment(query, series) match = sa.best_match() startidx, endidx = match.segment dtwvis.plot_warpingpaths(query, series, sa.warping_paths(), match.path, figure=fig)
The resultig match is
If we compare the best match with the query we see they are similar. The best match is only a little bit compressed.
If you want to find all matches (or the k best):
fig, ax = dtwvis.plot_warpingpaths(query, series, sa.warping_paths(), path=-1) for kmatch in sa.kbest_matches(9): dtwvis.plot_warpingpaths_addpath(ax, kmatch.path)
DTW subsequence search¶
Similar to using alignment, we can also iterate over a sequence of series or windows to search for the best match:
from dtaidistance.subsequence.dtw import subsequence_search k = 3 s =  w = 22 ws = int(np.floor(w/2)) wn = int(np.floor((len(series) - (w - ws)) / ws)) si, ei = 0, w for i in range(wn): s.append(series[si:ei]) si += ws ei += ws sa = subsequence_search(query, s) best = sa.kbest_matches(k=k)
When setting k, the search is pruned to early abandon comparisons that will not improve on the top k best matches.
In the result one can observe that the choice of windows has an impact on where the best matches are found. Whereas the previous alignment method does not require a window size or a shift, here matches are limited to the windows that are given. The advantage of this method is that it can be used also if the windows are not from one continuous series (e.g. periods with missing data, multiple sources).
The best three windows are visualized below. The gray vertical lines indicate the windows, the red verical lines the three best windows.
DTW Local Concurrences¶
In some case we are not interested in searching for a query but to find any or all subsequences that are similar between two series. This is used for example to identify that parts of two series are similar but not necessarily the entire series. Or when comparing a series to itself it produces subsequences (of arbitrary length) that frequenty reappear in the series.
For example below, we can see that one heartbeat in ECG is a common pattern. Sometimes a sequence a few heartbeats is similar to another sequence of heartbeats.
lc = local_concurrences(series, None, estimate_settings=0.7) # second is None to compare to self # The parameters tau, delta, delta_factor are estimated based on series paths =  for match in lc.kbest_matches(k=100, minlen=20, buffer=10): paths.append(match.path) fig, ax = dtwvis.plot_warpingpaths(series, series, lc.wp, path=-1) for path in paths: dtwvis.plot_warpingpaths_addpath(ax, path)