Description Usage Arguments Value Examples

This method implements the predicted value and their standard deviation according to
the regular difference time series model
*(1-B)^s y<sub>t</sub>=a<sub>t</sub>*.

1 2 3 4 5 6 7 8 9 | ```
StatRegDiffTSPred(x, StatDiff = 12L, forward = 2L, VarNames = NULL)
## S4 method for signature 'vector'
StatRegDiffTSPred(x, StatDiff = 12L, forward = 2L,
VarNames = NULL)
## S4 method for signature 'StQList'
StatRegDiffTSPred(x, StatDiff = 12L, forward = 2L,
VarNames = NULL)
``` |

`x` |
object upon which the prediction will be made. |

`StatDiff` |
stational differences of the time series; by default it is 12L. |

`forward` |
integer indicating the number of periods ahead when the prediction will be made; by default it is 2L. |

`VarNames` |
character vector with the variable names for which the prediction will be made; by default it is NULL. |

It returns a list with components Pred and STD, containing the point prediction and the estimated standard deviations, respectively. Depending on the class of the input parameter x, it returns:

For input class vector, it returns numeric vectors.

For input class matrix, it returns matrices.

For input class StQList, it returns list whose components are data.tables.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
# Predicting one and two months ahead in time
data(Example1.TS)
StatRegDiffTSPred(Example1.TS, forward = 1L)
StatRegDiffTSPred(Example1.TS, forward = 2L)
# Predicting upon a times series with many NA values
data(Example2.TS)
StatRegDiffTSPred(Example2.TS, forward = 1L)
# On a matrix
Mat <- rbind(Example1.TS, Example2.TS)
StatRegDiffTSPred(Mat, forward = 1L)
## Not run:
# With an object of class StQList
data(StQListExample)
VarNames <- c('ActivEcono_35._6._2.1.4._0', 'GeoLoc_35._6._2.1._1.2.5.')
StatRegDiffTSPred(StQListExample, StatDiff = 9L, VarNames = VarNames)
## End(Not run)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.