bforecast: Perform bootstrap SSA forecasting of the series

In Rssa: A Collection of Methods for Singular Spectrum Analysis

Perform bootstrap SSA forecasting of the series

Description

Perform bootstrap SSA forecasting of the one-dimensional series.

Usage

## S3 method for class '1d.ssa'
bforecast(x, groups, len = 1, R = 100, level = 0.95,
          type = c("recurrent", "vector"),
          interval = c("confidence", "prediction"),
          only.new = TRUE,
          only.intervals = FALSE, ...,
          drop = TRUE, drop.attributes = FALSE, cache = TRUE)
## S3 method for class 'toeplitz.ssa'
bforecast(x, groups, len = 1, R = 100, level = 0.95,
          type = c("recurrent", "vector"),
          interval = c("confidence", "prediction"),
          only.new = TRUE,
          only.intervals = FALSE, ...,
          drop = TRUE, drop.attributes = FALSE, cache = TRUE)

Arguments

x	SSA object holding the decomposition
groups	list, the grouping of eigentriples to be used in the forecast
len	the desired length of the forecasted series
R	number of bootstrap replications
level	vector of confidence levels for bounds
type	the type of forecast method to be used during bootstrapping
interval	type of interval calculation
only.new	logical, if 'FALSE' then confidence bounds for the signal as well as prediction are reported
only.intervals	logical, if 'TRUE' then bootstrap method is used for confidence bounds only, otherwise — mean bootstrap forecast is returned as well
...	additional arguments passed to forecasting routines
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

Details

The routine uses the reconstruction residuals in order to calculate their empirical distribution (the residuals are assumed to be stationary). Empirical distribution of the residuals is used to perform bootstrap series simulation. Such bootsrapped series are then extended via selected forecast method. Finally, the distribution of forecasted values is used to calculate bootstrap estimate of series forecast and confidence bounds.

See Section 3.2.1.5 from Golyandina et al (2018) for details.

Value

List of matricies. Each matrix has 1 + 2*length(level) columns and 'len' rows. First column contains the forecasted values, remaining columns — low and upper bootstrap confidence bounds for average forecasted values.

The matrix itself, if length of groups is one and 'drop = TRUE'.

References

Golyandina N., Korobeynikov A., Zhigljavsky A. (2018): Singular Spectrum Analysis with R. Use R!. Springer, Berlin, Heidelberg.

See Also

Rssa for an overview of the package, as well as, rforecast, vforecast, forecast.

Examples

# Decompose 'co2' series with default parameters
s <- ssa(co2)
# Produce 24 forecasted values and confidence bounds of the series using
# the first 3 eigentriples as a base space for the forecast.

f <- bforecast(s, groups = list(1:3), len = 24, R = 50)
matplot(f, col = c("black", "red", "red"), type='l')

Rssa documentation built on Aug. 23, 2022, 1:06 a.m.