Description Usage Arguments Details Value Author(s) References See Also Examples
Using the methods of Hyndman et al. (2016) and Hyndman et al. (2011), this function optimally combines
the forecasts at all levels of a hierarchical time series. The
forecast.gts
calls this function when the comb
method
is selected.
1 2 3 4 5 6 7 8 9 10 11 12 |
fcasts |
Matrix of forecasts for all levels of the hierarchical time series. Each row represents one forecast horizon and each column represents one time series from the hierarchy. |
nodes |
If the object class is |
groups |
If the object class is |
weights |
A numeric vector. The default is |
nonnegative |
Logical. Should the reconciled forecasts be non-negative? |
algorithms |
An algorithm to be used for computing reconciled
forecasts. See |
keep |
Return a |
parallel |
Logical. Import parallel package to allow parallel processing. |
num.cores |
Numeric. Specify how many cores are going to be used. |
control.nn |
A list of control parameters to be passed on to the block principal pivoting algorithm. See 'Details'. |
The control.nn
argument is a list that can supply any of the following components:
ptype
Permutation method to be used: "fixed"
or "random"
. Defaults to "fixed"
.
par
The number of full exchange rules that may be tried. Defaults to 10.
gtol
The tolerance of the convergence criteria. Defaults to sqrt(.Machine$double.eps)
.
Return the (non-negative) reconciled gts
object or forecasts at the bottom
level.
Alan Lee, Rob J Hyndman, Earo Wang and Shanika L Wickramasuriya
Hyndman, R. J., Ahmed, R. A., Athanasopoulos, G., & Shang, H. L. (2011). Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis, 55(9), 2579–2589. https://robjhyndman.com/publications/hierarchical/
Hyndman, R. J., Lee, A., & Wang, E. (2016). Fast computation of reconciled forecasts for hierarchical and grouped time series. Computational Statistics and Data Analysis, 97, 16–32. https://robjhyndman.com/publications/hgts/
Wickramasuriya, S. L., Turlach, B. A., & Hyndman, R. J. (to appear). Optimal non-negative forecast reconciliation. Statistics and Computing. https://robjhyndman.com/publications/nnmint/
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | # hts example
## Not run:
h <- 12
ally <- aggts(htseg1)
allf <- matrix(NA, nrow = h, ncol = ncol(ally))
for(i in 1:ncol(ally))
allf[,i] <- forecast(auto.arima(ally[,i]), h = h)$mean
allf <- ts(allf, start = 51)
y.f <- combinef(allf, get_nodes(htseg1), weights = NULL, keep = "gts", algorithms = "lu")
plot(y.f)
## End(Not run)
## Not run:
h <- 12
ally <- abs(aggts(htseg2))
allf <- matrix(NA, nrow = h, ncol = ncol(ally))
for(i in 1:ncol(ally))
allf[,i] <- forecast(auto.arima(ally[,i], lambda = 0, biasadj = TRUE), h = h)$mean
b.f <- combinef(allf, get_nodes(htseg2), weights = NULL, keep = "bottom",
algorithms = "lu")
b.nnf <- combinef(allf, get_nodes(htseg2), weights = NULL, keep = "bottom",
algorithms = "lu", nonnegative = TRUE)
## End(Not run)
# gts example
## Not run:
abc <- ts(5 + matrix(sort(rnorm(200)), ncol = 4, nrow = 50))
g <- rbind(c(1,1,2,2), c(1,2,1,2))
y <- gts(abc, groups = g)
h <- 12
ally <- aggts(y)
allf <- matrix(NA,nrow = h,ncol = ncol(ally))
for(i in 1:ncol(ally))
allf[,i] <- forecast(auto.arima(ally[,i]),h = h)$mean
allf <- ts(allf, start = 51)
y.f <- combinef(allf, groups = get_groups(y), keep ="gts", algorithms = "lu")
plot(y.f)
## End(Not run)
|
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