Description Usage Arguments Value Author(s) References See Also Examples
Using the method of Wickramasuriya et al. (2015), this function combines the
forecasts at all levels of a hierarchical or grouped time series. The
forecast.gts
calls this function when the MinT
method
is selected.
1 2 
fcasts 
Matrix of forecasts for all levels of a hierarchical or grouped time series. Each row represents one forecast horizon and each column represents one time series of aggregated or disaggregated forecasts. 
nodes 
If the object class is hts, a list contains the number of child nodes referring to hts. 
groups 
If the object is gts, a gmatrix is required, which is the same as groups in the function gts. 
residual 
Matrix of insample residuals for all the aggregated and
disaggregated time series. The columns must be in the same order as

covariance 
Type of the covariance matrix to be used. Shrinking towards a diagonal unequal variances ("shr") or sample covariance matrix ("sam"). 
algorithms 
Algorithm used to compute inverse of the matrices. 
keep 
Return a gts object or the reconciled forecasts at the bottom level. 
Return the reconciled gts
object or forecasts at the bottom
level.
Shanika L Wickramasuriya
Wickramasuriya, S. L., Athanasopoulos, G., & Hyndman, R. J. (2015). Forecasting hierarchical and grouped time series through trace minimization. Working paper 15/15, Department of Econometrics & Business Statistics, Monash University. http://robjhyndman.com/workingpapers/mint/
Hyndman, R. J., Lee, A., & Wang, E. (2015). Fast computation of reconciled forecasts for hierarchical and grouped time series. Computational Statistics and Data Analysis, 97, 16–32. http://robjhyndman.com/papers/hgts/
hts
, gts
,
forecast.gts
, combinef
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 40 41 42 43  # hts example
## Not run: h < 12
ally < aggts(htseg1)
n < nrow(ally)
p < ncol(ally)
allf < matrix(NA, nrow = h, ncol = p)
res < matrix(NA, nrow = n, ncol = p)
for(i in 1:p)
{
fit < auto.arima(ally[, i])
allf[, i] < forecast(fit, h = h)$mean
res[, i] < na.omit(ally[, i]  fitted(fit))
}
allf < ts(allf, start = 51)
y.f < MinT(allf, get_nodes(htseg1), residual = res, covariance = "shr",
keep = "gts", algorithms = "lu")
plot(y.f)
y.f_cg < MinT(allf, get_nodes(htseg1), residual = res, covariance = "shr",
keep = "all", algorithms = "cg")
## End(Not run)
# gts example
## Not run: abc < ts(5 + matrix(sort(rnorm(200)), ncol = 4, nrow = 50))
g < rbind(c(1,1,2,2), c(1,2,1,2))
y < gts(abc, groups = g)
h < 12
ally < aggts(y)
n < nrow(ally)
p < ncol(ally)
allf < matrix(NA,nrow = h,ncol = ncol(ally))
res < matrix(NA, nrow = n, ncol = p)
for(i in 1:p)
{
fit < auto.arima(ally[, i])
allf[, i] < forecast(fit, h = h)$mean
res[, i] < na.omit(ally[, i]  fitted(fit))
}
allf < ts(allf, start = 51)
y.f < MinT(allf, groups = get_groups(y), residual = res, covariance = "shr",
keep = "gts", algorithms = "lu")
plot(y.f)
## End(Not run)

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