Joint block bootstrap for generating probabilistic base forecasts that take into account the correlation between different temporal aggregation orders (Girolimetto et al. 2023).
teboot(model_list, boot_size, agg_order, block_size = 1, seed = NULL)A list with two elements: the seed used to sample the errors and a (\(\text{boot\_size}\times (k^\ast+m)\text{block\_size}\)) matrix.
A list of all the \((k^\ast+m)\) base forecasts models ordered
from the lowest frequency (most temporally aggregated) to the highest frequency.
A simulate() function for each model has to be available and implemented
according to the package forecast,
with the following mandatory parameters: object, innov,
future, and nsim.
The number of bootstrap replicates.
Highest available sampling frequency per seasonal cycle (max. order of temporal aggregation, \(m\)), or a vector representing a subset of \(p\) factors of \(m\).
Block size of the bootstrap, which is typically equivalent to the forecast horizon for the most temporally aggregated series.
An integer seed.
Girolimetto, D., Athanasopoulos, G., Di Fonzo, T. and Hyndman, R.J. (2023), Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues. International Journal of Forecasting, in press. tools:::Rd_expr_doi("10.1016/j.ijforecast.2023.10.003")
Bootstrap samples:
csboot(),
ctboot()
Temporal framework:
tebu(),
tecov(),
telcc(),
temo(),
terec(),
tetd(),
tetools()