# mvm

0th

Percentile

##### Minimum Variance Matching algorithm

Step patterns to compute the Minimum Variance Matching (MVM) correspondence between time series

Keywords
ts
##### Usage
mvmStepPattern(elasticity=20);
##### Arguments
elasticity

integer: maximum consecutive reference elements skippable

##### Details

The Minimum Variance Matching algorithm [1] 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.

The mvmStepPattern function creates a stepPattern object 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.

The 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).

##### Value

A step pattern object.

##### References

[1] Latecki, L. J.; Megalooikonomou, V.; Wang, Q. & Yu, D. An elastic partial shape matching technique Pattern Recognition, 2007, 40, 3069-3080

[2] 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 stepPattern.
library(dtw) # NOT RUN { ## The hand-checkable example given in ref. [1] 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") # }