# 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 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

Latecki, L. J.; Megalooikonomou, V.; Wang, Q. & Yu, D.
*An elastic partial shape matching technique* Pattern Recognition,
2007, 40, 3069-3080. http://dx.doi.org/10.1016/j.patcog.2007.03.004

##### See Also

Other objects in `stepPattern()`

.

##### Examples

```
# NOT RUN {
## The hand-checkable example given in Fig. 5, 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")
# }
```

*Documentation reproduced from package dtw, version 1.21-3, License: GPL (>= 2)*