Perform vector SSA forecasting of the series
Perform vector SSA forecasting of the series.
## S3 method for class '1d.ssa' vforecast(x, groups, len = 1, only.new = TRUE, ..., drop = TRUE, drop.attributes = FALSE) ## S3 method for class 'toeplitz.ssa' vforecast(x, groups, len = 1, only.new = TRUE, ..., drop = TRUE, drop.attributes = FALSE) ## S3 method for class 'toeplitz.ssa' vforecast(x, groups, len = 1, only.new = TRUE, ..., drop = TRUE, drop.attributes = FALSE) ## S3 method for class 'mssa' vforecast(x, groups, len = 1, direction = c("row", "column"), only.new = TRUE, ..., drop = TRUE, drop.attributes = FALSE) ## S3 method for class 'cssa' vforecast(x, groups, len = 1, only.new = TRUE, ..., drop = TRUE, drop.attributes = FALSE) ## S3 method for class 'pssa.1d.ssa' vforecast(x, groups, len = 1, only.new = TRUE, ..., drop = TRUE, drop.attributes = FALSE)
x: SSA object holding the decomposition
groups: list, the grouping of eigentriples to be used in the forecast
len: integer, the desired length of the forecasted series
direction: direction of forecast in multichannel SSA case, "column" stands for so-called L-forecast and "row" stands for K-forecast
only.new: logical, if 'TRUE' then only forecasted values are returned, whole series otherwise
...: additional arguments passed to decompose
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.
The routines applies the vector SSA forecasting algorithm to produce the new series which is expected to 'continue' the current series on the basis of a given decomposition. Vector forecast differs from recurrent forecast in such a way that it continues the set of vectors in the subspace spanning the chosen eigenvectors (the same formula as described in lrr is used for constructing of the last components of the new vectors) and then derives the series out of this extended set of vectors.
For multichannel SSA, forecast can be constructed in two versions, row and column ones; it uses the formulae from Golyandina et al (2015).
For details of 1D-SSA recurrent forecasting, see Section 3.2.1.3 and Algorithm 3.6 in Golyandina et al (2018). For details of MSSA recurrent forecasting, see Section 4.3.1.3 and Algorithm 4.5 (column forecasting).
List of forecasted objects. 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'.
Golyandina N., Korobeynikov A., Zhigljavsky A. (2018): Singular Spectrum Analysis with R. Use R!. Springer, Berlin, Heidelberg.
Golyandina, N., Nekrutkin, V. and Zhigljavsky, A. (2001): Analysis of Time Series Structure: SSA and related techniques. Chapman and Hall/CRC. ISBN 1584881941
Golyandina, N. and Stepanov, D. (2005): SSA-based approaches to analysis and forecast of multidimensional time series. In Proceedings of the 5th St.Petersburg Workshop on Simulation, June 26-July 2, 2005, St. Petersburg State University, St. Petersburg, 293--298. https://www.gistatgroup.com/gus/mssa2.pdf
Golyandina, N., Korobeynikov, A., Shlemov, A. and Usevich, K. (2015): Multivariate and 2D Extensions of Singular Spectrum Analysis with the Rssa Package. Journal of Statistical Software, Vol. 67, Issue 2. tools:::Rd_expr_doi("10.18637/jss.v067.i02")
Rssa for an overview of the package, as well as, rforecast, bforecast, forecast.
# Decompose 'co2' series with default parameters s <- ssa(co2) # Produce 24 forecasted values of the series using different sets of eigentriples # as a base space for the forecast. vfor <- vforecast(s, groups = list(c(1,4), 1:4), len = 24, only.new=FALSE) matplot(data.frame(c(co2, rep(NA, 24)), vfor), type="l") # Forecast `co2' trend by SSA with projections s <- ssa(co2, column.projector = 2, row.projector = 2) len <- 100 vfor <- vforecast(s, groups = list(trend = seq_len(nspecial(s))), len = len, only.new = FALSE) matplot(data.frame(c(co2, rep(NA, len)), vfor), type = "l") # Forecast finite rank series with polynomial component by SSA with projections v <- 5000 * sin(2*pi / 13 * (1:100)) + (1:100)^2 + 10000 s <- ssa(v, row.projector = 2, column.projector = 2) plot(vforecast(s, groups = list(all = 1:6), len = 100, only.new = FALSE), type = "l")