R: Generate Probabilistic Climatological Ensemble Forecast from...

generateRef {easyVerification} | R Documentation

## Generate Probabilistic Climatological Ensemble Forecast from Observations

### Description

To generate reference ensemble forecasts for forecast evaluation based on the available observations, `indRef` implements the out-of-sample or in-sample protocol to be used and `generateRef` produces the corresponding ensemble forecast given the actual observations.

### Usage

    indRef(nfcst, type = c("none", "forward", "crossval", "block"),
      indices = 1:nfcst, blocklength = 1)

    generateRef(obs, ind)

### Arguments

`nfcst` | number of forecast instances to be produce
`type` | type of out-of-sample protocol to be applied (see below)
`indices` | Subset of the observations / forecast times to be used for reference forecasts
`blocklength` | for cross-validation and split-sample
`obs` | vector of observations
`ind` | list or matrix of dimension (`n x nref`) of indices of the observations to be used for each forecast instance

### Value

`ind` | A list of indices to be used for each forecast from `1` to `nfcst`

### Cross-validation

Leave-one-out and leave-n-out cross-validation reference forecasts can be produced by setting `type = "crossval"`. By default, the blocklength is set to `1`, but moving blocks of length `n` can be specified by setting `blocklength = n`.

### Split sample

In contrast to `type="crossval"`, `type="block"` is used for split-sample validation with non-overlapping blocks of length `blocklength` retained for validation.

### Forward

Correspondingly, reference forecasts that are only based on past (future) observations can be produced using `type = "forward"`. For this, the first half of the reference forecasts only uses future information, i.e. observations `2:n` for forecast `1`, `3:n` for `2` and so forth. The second half of the reference forecasts use only past observations, i.e. observations `1:(n-1)` for forecast `n`, `1:(n-2)` for `n-1`, etc.

### Subsetting

In combination with the above, a subset of the observations can be specified for use as reference forecasts by providing the explicit indices of the observations to be used via `indices=1:k`. In combination with the `forward` method, all observations in `indices` will be used to construct the reference forecast for forecasts not included in `indices` (i.e. if `nfcst > max(indices)`).

[Package easyVerification version 0.4.4 Index]