gives the dynamic time warping (DTW) distance between sequences s1 and s2.


uses a window specified by win for local search.

Details and Options


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Basic Examples  (1)

Find the time warping distance between two sequences of values:

Show the correspondence between the sequences:

Scope  (10)

Data  (7)

Find the time warping distance between two real-valued vectors:

Find the time warping distance between two sequences of 2D points:

Show the correspondence between the sequences:

Find the time warping distance between two Boolean vectors:

Sequences of Boolean-valued vectors:

Sequences of quantities with compatible units:

All units are converted to base SI units:

Two-dimensional quantity arrays:

Find the distance between a sequence of quantities and a sequence of scalars:

The scalars are interpreted as if they had a compatible unit from SI base units:

Search Window  (3)

By default, the search is not locally constrained:

Specify a radius for the search window:

Increase the radius to find a more optimal alignment:

A window size is interpreted as the radius of a slanted band window:

Use a band of radius 1:

Use a parallelogram of slope 3:

For signals of equal length, the "Band" window is equivalent to the "SlantedBand" window:

Generalizations & Extensions  (1)

Compute DTW-Delta (or DTW-D) by normalizing DTW by the Euclidean distance of sequences:

Comparing a sine wave, a distorted sine wave, and a random noise using DTW:

Using regular DTW, the sine wave seems to be more similar to the random noise:

Using DTW-Delta, the sine wave is more similar to the distorted sine wave:

Options  (6)

DistanceFunction  (5)

With Boolean sequences, WarpingDistance uses MatchingDissimilarity:

Compare two sequences using different distance functions:

In case of one-dimensional signals, some distance functions are equivalent:

In case of one-dimensional signals, NormalizedSquaredEuclideanDistance and CorrelationDistance always return 0:

For signals containing one element, time warping distance is equal to the distance between elements:

Method  (1)

By default, the query sequence will be matched with the entire reference sequence:

With "MatchingInterval""FlexibleEnd", any suffix of reference sequence can be omitted:

With "MatchingInterval""Flexible", any suffix and prefix of reference can be omitted:

Applications  (6)

Cluster time series of normalized stock values:

Find the nearest stock based on normalized stock values:

Find the capital city in the EU that had the most similar temperature to Chicago over the last year. Use WarpingDistance to determine the similarity between temperature sequences:

Get the temperature for all capital cities in the European Union:

Find the capital city that has the most similar temperatures to Chicago:

Compute pairwise distances between temperature time series of all EU capitals:

Visualize the temperature similarity using a previously computed distance matrix:

Compare daily mean humidity for some cities in 2014:

Extract humidity of the cities:

Compare extracted data:

Compare two ECG signals of heartbeats:

Find the distance between the two signals:

Properties & Relations  (8)

The two sequences need not have the same length:

The distance between two equal sequences is always 0:

Relation with WarpingCorrespondence:

Find the time warped sequences using the correspondence:

WarpingDistance gives the sum of all the distances between corresponding elements:

Smaller window radius results in faster computation:

However, using smaller radius may lead to a less optimal alignment, resulting in higher distance values:

WarpingDistance is a symmetric function:

The "triangle inequality" does not hold:

Dynamic time warping is not translation invariant:

Canonical time warping is translation invariant:

The time warping distance increases for longer signals:

Normalization allows a more reasonable comparison:

Dividing by the length of the correspondence path is also a common way of normalization:

Possible Issues  (3)

The search window specified may be too narrow to contain a correspondence path. In that case it will be automatically widened:

Two long, similar signals could have a higher distance compared to two short, dissimilar signals:

Comparing two short, random sequences, which gives a relatively smaller distance:

Divide by the length of signals:

Divide by the length of the correspondence path:

Quantities in input sequences must have compatible units:

Introduced in 2016