FoReco-hts {FoReco}

Simple examples to compare FoReco and hts packages

Description

Two datasets of the hts package are used to show how to get the same results using FoReco. First, we consider the htseg1 dataset (a simulated three level hierarchy, with a total of 8 series, each of length 10). Then, we take the htseg2 dataset (a simulated four level hierarchy with a total of 17 series, each of length 16). htseg1 and htseg2 are objects of class hts in hts.

References

Hyndman, R. J., Lee, A., Wang, E., and Wickramasuriya, S. (2020). hts: Hierarchical and Grouped Time Series, R package version 6.0.1, https://CRAN.R-project.org/package=hts.

Examples

## Not run: 
library(hts)
require(FoReco)

####### htseg1 #######
data <- allts(htseg1)
n <- NCOL(data)
nb <- NCOL(htseg1$bts)
na <- n-nb
C <- smatrix(htseg1)[1:na, ]

# List containing the base forecasts
# Forecast horizon: 10
base <- list()
for (i in 1:n) {
  base[[i]] <- forecast(auto.arima(data[, i]))
}

# Create the matrix of base forecasts
BASE <- NULL
for (i in 1:n) {
  BASE <- cbind(BASE, base[[i]]$mean)
}
colnames(BASE) <- colnames(data)

# Create the matrix of residuals
res <- NULL
for (i in 1:n) {
  res <- cbind(res, base[[i]]$residuals)
}
colnames(res) <- colnames(data)

## Comparisons
# ols
# two commands in hts...
Y_hts_forecast <- forecast(htseg1, method = "comb", fmethod = "arima", weights = "ols")
Y_hts_ols <- combinef(BASE, nodes = get_nodes(htseg1), keep = "all")
# ...with the same results:
sum(abs(allts(Y_hts_forecast) - Y_hts_ols) > 1e-10)

Y_FoReco_ols <- htsrec(BASE, C = C, comb = "ols")$recf
sum(abs(Y_hts_ols - Y_FoReco_ols) > 1e-10)

# struc
w <- 1 / apply(smatrix(htseg1), 1, sum)
Y_hts_struc <- combinef(BASE, nodes = get_nodes(htseg1), weights = w, keep = "all")
Y_FoReco_struc <- htsrec(BASE, C = C, comb = "struc")$recf
sum(abs(Y_hts_struc - Y_FoReco_struc) > 1e-10)

# shr
Y_hts_shr <- MinT(BASE, nodes = get_nodes(htseg1), keep = "all",
                  covariance = "shr", residual = res)
Y_FoReco_shr <- htsrec(BASE, C = C, comb = "shr", res = res)$recf
sum(abs(Y_hts_shr - Y_FoReco_shr) > 1e-10)

# sam - hts error "MinT needs covariance matrix to be positive definite."
#       The covariance matrix is ill-conditioned, hts considers it as non-invertible
Y_hts_sam <- MinT(BASE, nodes = get_nodes(htseg1), keep = "all",
                  covariance = "sam", residual = res)
Y_FoReco_sam <- htsrec(BASE, C = C, comb = "sam", res = res)$recf
# sum((Y_hts_sam-Y_FoReco_sam)>1e-10)

####### htseg2 #######
data <- allts(htseg2)
n <- NCOL(data)
nb <- NCOL(htseg2$bts)
na <- n-nb
C <- smatrix(htseg2)[1:na, ]

## Computation of the base forecasts
# using the auto.arima() function of the package forecast (loaded by hts)
# List containing the base forecasts
# Forecast horizon: 10
base <- list()
for (i in 1:n) {
  base[[i]] <- forecast(auto.arima(data[, i]))
}

# Create the matrix of base forecasts
BASE <- NULL
for (i in 1:n) {
  BASE <- cbind(BASE, base[[i]]$mean)
}
colnames(BASE) <- colnames(data)

# Create the matrix of residuals
res <- NULL
for (i in 1:n) {
  res <- cbind(res, base[[i]]$residuals)
}
colnames(res) <- colnames(data)

## Comparisons
# ols
Y_hts_ols <- combinef(BASE, nodes = get_nodes(htseg2), keep = "all")
Y_FoReco_ols <- htsrec(BASE, C = C, comb = "ols")$recf
sum(abs(Y_hts_ols - Y_FoReco_ols) > 1e-10)

# struc
w <- 1 / apply(smatrix(htseg2), 1, sum)
Y_hts_struc <- combinef(BASE, nodes = get_nodes(htseg2), weights = w, keep = "all")
Y_FoReco_struc <- htsrec(BASE, C = C, comb = "struc")$recf
sum(abs(Y_hts_struc - Y_FoReco_struc) > 1e-10)

# shr
Y_hts_shr <- MinT(BASE, nodes = get_nodes(htseg2), keep = "all", covariance = "shr", residual = res)
Y_FoReco_shr <- htsrec(BASE, C = C, comb = "shr", res = res)$recf
sum(abs(Y_hts_shr- Y_FoReco_shr) > 1e-10)

## End(Not run)