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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 with two elements: the seed used to sample the errors and a (boot\_size h(k^+m)) matrix
A list of (k^+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.
Girolimetto, D., Athanasopoulos, G., Di Fonzo, T., & Hyndman, R. J. (2023), Cross-temporal Probabilistic Forecast Reconciliation, tools:::Rd_expr_doi("10.48550/arXiv.2303.17277").
Other bootstrap:
boot_cs(),
boot_ct()