Description Usage Arguments Details Value See Also Examples

All-in-one function to perform SSA forecasting of one-dimensional series.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | ```
## S3 method for class '1d.ssa'
forecast(object,
groups, h = 1,
method = c("recurrent", "vector"),
interval = c("none", "confidence", "prediction"),
only.intervals = TRUE,
...,
drop = TRUE, drop.attributes = FALSE, cache = TRUE)
## S3 method for class 'toeplitz.ssa'
forecast(object,
groups, h = 1,
method = c("recurrent", "vector"),
interval = c("none", "confidence", "prediction"),
only.intervals = TRUE,
...,
drop = TRUE, drop.attributes = FALSE, cache = TRUE)
## S3 method for class '1d.ssa'
predict(object,
groups, len = 1,
method = c("recurrent", "vector"),
interval = c("none", "confidence", "prediction"),
only.intervals = TRUE,
...,
drop = TRUE, drop.attributes = FALSE, cache = TRUE)
## S3 method for class 'toeplitz.ssa'
predict(object,
groups, len = 1,
method = c("recurrent", "vector"),
interval = c("none", "confidence", "prediction"),
only.intervals = TRUE,
...,
drop = TRUE, drop.attributes = FALSE, cache = TRUE)
## S3 method for class 'mssa'
predict(object,
groups, len = 1,
method = c("recurrent", "vector"),
direction = c("column", "row"),
...,
drop = TRUE, drop.attributes = FALSE, cache = TRUE)
``` |

`object` |
SSA object holding the decomposition |

`groups` |
list, the grouping of eigentriples to be used in the forecast |

`h,len` |
the desired length of the forecasted series |

`method` |
method of forecasting to be used |

`interval` |
type of interval calculation |

`only.intervals` |
logical, if 'TRUE' then bootstrap method is used for confidence bounds only, otherwise — mean bootstrap forecast is returned as well |

`direction` |
direction of forecast in multichannel SSA case, "column" stands for so-called L-forecast and "row" stands for K-forecast |

`...` |
further arguments passed for forecast routines
(e.g. |

`drop` |
logical, if 'TRUE' then the result is coerced to series itself, when possible (length of 'groups' is one) |

`drop.attributes` |
logical, if 'TRUE' then the forecast routines do not try to infer the time index arguments for the forecasted series. |

`cache` |
logical, if 'TRUE' then intermediate results will be cached in the SSA object. |

This function is a convenient wrapper over other forecast routines (see 'See Also') turning their value into object of type 'forecast' which can be used with the routines from forecast package.

object of class 'forecast' for `forecast`

function call,
predicted series for `predict`

call.

`Rssa`

for an overview of the package, as well as,
`rforecast`

,
`vforecast`

,
`bforecast`

,
`forecast (package)`

1 2 3 4 5 6 7 8 9 10 | ```
s <- ssa(co2)
# Calculate 24-point forecast using first 6 components as a base
f <- forecast(s, groups = list(1:6), method = "recurrent", bootstrap = TRUE, len = 24, R = 10)
# Plot the result including the last 24 points of the series
plot(f, include = 24, shadecols = "green", type = "l")
# Use of predict() for prediction
p <- predict(s, groups = list(1:6), method = "recurrent", len = 24)
# Simple plotting
plot(p, ylab = "Forecasteed Values")
``` |

Rssa documentation built on Oct. 4, 2017, 5:06 p.m.

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