mvm
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 noncontiguous
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, asymmetriclike 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, 30693080 [2] Toni Giorgino. Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package. Journal of Statistical Software, 31(7), 124. http://www.jstatsoft.org/v31/i07/
See Also
Other objects in stepPattern
.
Examples
## The handcheckable 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")