Perform SSA gapfilling of the series.

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## S3 method for class '1d.ssa'
gapfill(x, groups, base = c("original", "reconstructed"),
method = c("sequential", "simultaneous"),
alpha = function(len) seq.int(0, 1, length.out = len), ...,
drop = TRUE, drop.attributes = FALSE, cache = TRUE)
## S3 method for class 'mssa'
gapfill(x, groups, base = c("original", "reconstructed"),
alpha = function(len) seq.int(0, 1, length.out = len), ...,
drop = TRUE, drop.attributes = FALSE, cache = TRUE)
## S3 method for class 'cssa'
gapfill(x, groups, base = c("original", "reconstructed"),
method = c("sequential", "simultaneous"),
alpha = function(len) seq.int(0, 1, length.out = len), ...,
drop = TRUE, drop.attributes = FALSE, cache = TRUE)
## S3 method for class 'toeplitz.ssa'
gapfill(x, groups, base = c("original", "reconstructed"),
method = c("sequential", "simultaneous"),
alpha = function(len) seq.int(0, 1, length.out = len), ...,
drop = TRUE, drop.attributes = FALSE, cache = TRUE)
``` |

`x` |
Shaped SSA object holding the decomposition |

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

`base` |
series used as a 'seed' for gapfilling: original or
reconstructed according to the value of |

`method` |
method used for gapfilling, "sequential" means to filling by a recurrent forecast from complete parts; "simultaneous" tries to build a projections onto the signal subspace. See 'References' for more info. |

`alpha` |
weight used for combining forecasts from left and right when method = "sequential"; 0.5 means that the forecasts are averaged, 0 (1) means that only forecast from the left (right correspondingly) is used, arbitrary function could be specified; by default linear weights are used. |

`...` |
additional arguments passed to |

`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 attributes of the input series are not copied to the reconstructed ones. |

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

The function fills in the missed entries in the series. Both methods described in (Golyandina, Osipov, 2007) are implemented:

method = "sequential" performs forecast from complete chunks onto incomplete. For internal gaps forecast is performed from both sides of the gap and average is taken in order to reduce the forecast error. For gaps in the beginning or end of the series the method coincides with ordinary recurrent forecast;

method = "simultaneous" performs gap filling via projections onto signal subspace. The method may fail if insufficient complete observations are provided.

List of objects with gaps filled in. Elements of the list have the
same names as elements of `groups`

. If group is unnamed,
corresponding component gets name ‘Fn’, where ‘n’ is its index in
`groups`

list.

Or, the forecasted object itself, if length of groups is one and 'drop = TRUE'.

N. Golyandina, E. Osipov (2007): *The "Caterpillar"-SSA method
for analysis of time series with missing values*. Journal of
Statistical Planning and Inference, Vol. 137, No. 8, Pp 2642–2653
http://www.gistatgroup.com/cat/mvssa1en.pdf

`Rssa`

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

,
`igapfill`

,
`clplot`

,
`summarize.gaps`

,

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