Minimum Variance Matching algorithm
Step patterns to compute the Minimum Variance Matching (MVM) correspondence between time series
- integer: maximum consecutive reference elements skippable
The Minimum Variance Matching algorithm  finds the non-contiguous parts of reference which best match the query, allowing for arbitrarily long "stretches" of reference to be excluded from the match. All elements of the query have to be matched. First and last elements of the query are anchored at the boundaries of the reference.
mvmStepPattern function creates a
which implements this behavior, to be used with the usual
dtw call (see example). MVM is computed as a special
case of DTW, with a very large, asymmetric-like step pattern.
elasticity argument limits the maximum run length of
reference which can be skipped at once. If no limit is desired, set
elasticity to an integer at least as large as the reference
(computation time grows linearly).
A step pattern object.
 Latecki, L. J.; Megalooikonomou, V.; Wang, Q. & Yu, D. An elastic partial shape matching technique Pattern Recognition, 2007, 40, 3069-3080  Toni Giorgino. Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package. Journal of Statistical Software, 31(7), 1-24. http://www.jstatsoft.org/v31/i07/
Other objects in
## The hand-checkable example given in ref.  above diffmx <- matrix( byrow=TRUE, nrow=5, c( 0, 1, 8, 2, 2, 4, 8, 1, 0, 7, 1, 1, 3, 7, -7, -6, 1, -5, -5, -3, 1, -5, -4, 3, -3, -3, -1, 3, -7, -6, 1, -5, -5, -3, 1 ) ) ; ## Cost matrix costmx <- diffmx^2; ## Compute the alignment al <- dtw(costmx,step.pattern=mvmStepPattern(10)) ## Elements 4,5 are skipped print(al$index2) plot(al,main="Minimum Variance Matching alignment")