Joint block bootstrap for generating probabilistic base forecasts that take into account the correlation between different temporal aggregation order (Girolimetto et al. 2023).
boot_te(fit, boot_size, m, h = 1, seed = NULL)A list of \((k^\ast+m)\) base forecast models ordered as
[lowest_freq' ... highest_freq']'. It is important to note that the models
must have the simulate() function available and implemented as with
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 subset of \(p\) factors of \(m\).
Forecast horizon for the most temporally aggregated series.
An integer seed.
A list with two elements: the seed used to sample the errors and a (\(boot\_size\times h(k^\ast+m)\)) matrix
Girolimetto, D., Athanasopoulos, G., Di Fonzo, T., & Hyndman, R. J. (2023), Cross-temporal Probabilistic Forecast Reconciliation, doi:10.48550/arXiv.2303.17277 .