| Type: | Package |
| Title: | Degenerate Hierarchical Time Series Reconciliation |
| Version: | 0.1.0 |
| Description: | Takes the MinT implementation of the 'hts'https://cran.r-project.org/package=hts package and adapts it to allow degenerate hierarchical structures. Instead of the "nodes" argument, this function takes an S matrix which is more versatile in the structures it allows. For a demo, see Steinmeister and Pauly (2024)<doi:10.15488/17729>. The MinT algorithm is based on Wickramasuriya et al. (2019)<doi:10.1080/01621459.2018.1448825>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| Imports: | SparseM, methods, stats |
| Suggests: | forecast, testthat, knitr, rmarkdown, covr |
| RoxygenNote: | 7.3.1 |
| NeedsCompilation: | no |
| Packaged: | 2024-12-09 22:26:24 UTC; Louis |
| Author: | Louis Steinmeister [aut, cre], Markus Pauly [aut] |
| Maintainer: | Louis Steinmeister <louis.steinmeister@udo.edu> |
| Repository: | CRAN |
| Date/Publication: | 2024-12-10 21:10:02 UTC |
Bottom-up reconciliation
Description
Bottom-up reconciliation
Usage
BU(fcasts, S)
Arguments
fcasts |
forecasts to be reconciled |
S |
structure matrix representing the hierarchical structure of the hts |
Value
reconciled forecasts
Using the method of Wickramasuriya et al. (2019), this function (based on Hyndman et al.'s hts library) combines the forecasts at all levels of a hierarchical time series and works for degenerate hierarchies.
Description
Using the method of Wickramasuriya et al. (2019), this function (based on Hyndman et al.'s hts library) combines the forecasts at all levels of a hierarchical time series and works for degenerate hierarchies.
Usage
MinT(
fcasts,
Smat,
residual,
covariance = c("shr", "sam", "custom"),
nonnegative = FALSE,
cov.type = "complete.obs",
cov.matrix = NULL
)
Arguments
fcasts |
a vector or a matrix (rows = horizon, columns = ts columns) of forecasts |
Smat |
a structure matrix detailing the hierarchical structure of the hts. Make sure that the order of the rows align with the order of the forecasts. |
residual |
a matrix of in-sample residuals (columns = ts columns) |
covariance |
should a shrinkage estimator or the sample estimator be used? alternatively, a custom covariance matrix can be passed (additionally requires the cov.matrix argument) |
nonnegative |
not implemented yet. |
cov.type |
specify how the covariance matrix should be computed (default = complete observations). Note that pairwise.complete.obs may not yield a positive definite matrix! |
cov.matrix |
specify in case a custom covariance matrix should be used |
Value
reconciled forecasts
References
[hts: Hierarchical and Grouped Time Series]<https://cran.r-project.org/package=hts> [Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization]<doi:10.1080/01621459.2018.1448825> [Degenerate Hierarchical Time Series Reconciliation With The Minimum Trace Algorithm in R]<doi:10.15488/17729>
Examples
# Set the seed for reproducibility
set.seed(123)
# Create a sequence of 120 numbers
x <- seq(1, 120)
# Generate the columns
AA <- sin(x*pi/6) + rnorm(120, 0, 1) # Sine component with random noise
AB <- 0.05*x + rnorm(120, 0, 0.5) # Linear component
B <- cos(x*pi/6)+ rnorm(120, 0, 1) # Cosine component
# Combine the columns into a matrix
matrix <- cbind(AA, AB, B)
hts = ts(matrix, frequency = 12)
# Define S matrix
S <- rbind(c(1,1,1), c(1,1,0), diag(1,3))
rownames(S) <-c("Total", "A", "AA", "AB", "B")
colnames(S) <- c("AA", "AB", "B")
# Aggregate hts on all levels
hts.complete <- ts(t(S %*% t(hts)), frequency = 12)
# Fit a model to the time series
hts.models = lapply(hts.complete, function(c.ts) forecast::ets(c.ts))
# Fit a model to the time series
hts.models = lapply(hts.complete, function(c.ts) forecast::ets(c.ts))
# Generate predictions based on this model
hts.forecasts = sapply(hts.models, function(mdl) forecast::forecast(mdl, h = 1)$mean)
# Extract residuals
hts.residuals = sapply(hts.models, function(mdl) mdl$residuals)
# Compute reconciled forecasts
MinT(fcasts = hts.forecasts, Smat = S, residual = hts.residuals)
accuracy.gts
Description
accuracy.gts
Usage
accuracy.gts(fcasts, actuals)
Arguments
fcasts |
forecasts to be evaluated |
actuals |
actuals to compare the forecasts against |
Value
Averaged error measures across all time series in matrix form.
Structural Scaling reconciliation
Description
Structural Scaling reconciliation
Usage
strucScaling(fcasts, Smat, weights = rowSums(Smat))
Arguments
fcasts |
forecasts to be reconciled |
Smat |
structure matrix representing the hierarchical structure of the hts |
weights |
use the default for structural scaling and a vector of the residual variances for variance scaling |
Value
reconciled forecasts