Resampling syntax
Manually resampling
Because both the indices and the values of UncertainIndexValueDatasets are datasets themselves, you could manually resample them by accessing the indices and values fields. This gives you full control of the resampling.
There are some built-in sampling routines you could use instead if you use cases are simple.
Built-in resampling methods
Sequential constraints are always interpreted as belonging to the indices of an uncertain index-value dataset. Therefore, when using the built-in function to resample an index-value dataset, you can use the same syntax as for any other uncertain value dataset, but provide an additional sequential constraint after the regular constraints. The order of arguments is always 1) regular constraints, then 2) the sequential constraint.
The following examples illustrates the syntax. Assume udata is an UncertainIndexValueDataset instance. Then
resample(udata, StrictlyIncreasing())enforces the sequential constraint only to the indices, applying no constraint(s) on the furnishing distributions of either the indices nor the values of the dataset.resample(udata, StrictlyIncreasing(), TruncateQuantile(0.1, 0.9))applies the truncating constraint both the indices and the values, then enforces the sequential constraint on the indices.resample(udata, StrictlyIncreasing(), TruncateStd(2), TruncateQuantile(0.1, 0.9))applies separate truncating constraints to the indices and to the values, then enforces the sequential constraint on the indices.resample(udata, StrictlyIncreasing(), NoConstraint(), TruncateQuantile(0.1, 0.9))does the same as above, butNoConstraint()indicates that no constraints are applied to the indices prior to drawing the sequential realization of the indices.
Of course, like for uncertain value datasets, you can also apply individual constraints to each index and each value in the dataset, by providing a vector of constraints instead of a single constraint.
Currently implemented sequential constraints: