# NOT RUN {
library(clr)
data(gb_load)
data(clust_train)
clr_load <- clrdata(x = gb_load$ENGLAND_WALES_DEMAND,
order_by = gb_load$TIMESTAMP,
support_grid = 1:48)
## data cleaning: replace zeros with NA
clr_load[rowSums((clr_load == 0) * 1) > 0, ] <- NA
matplot(t(clr_load), ylab = 'Daily loads', type = 'l')
Y <- clr_load[2:nrow(clr_load), ]
X <- clr_load[1:(nrow(clr_load) - 1), ]
begin_pred <- which(substr(rownames(Y), 1, 4) == '2016')[1]
Y_train <- Y[1:(begin_pred - 1), ]
X_train <- X[1:(begin_pred - 1), ]
## Example without any cluster
model <- clr(Y = Y_train, X = X_train)
## Example with clusters
model <- clr(Y = Y_train, X = X_train, clust = clust_train)
# }
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