boot_ct: Cross-temporal Joint Bootstrap

In FoReco: Forecast Reconciliation

Cross-temporal Joint Bootstrap

Description

Joint block bootstrap for generating probabilistic base forecasts that take into account the correlation between different time series and temporal aggregation order at the same time (Girolimetto et al. 2023).

Usage

boot_ct(fit, boot_size, m, h = 1, seed = NULL)

Arguments

fit	A list of \mjseqnn elements. Each elements is a list with the \mjseqn(k^\ast+m) base forecast models ordered as [lowest_freq' ... highest_freq']' of the cross-sectional series. 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.
boot_size	The number of bootstrap replicates.
m	Highest available sampling frequency per seasonal cycle (max. order of temporal aggregation, \mjseqnm), or a subset of \mjseqnp factors of \mjseqnm.
h	Forecast horizon for the most temporally aggregated series.
seed	An integer seed.

Value

A list with two elements: the seed used to sample the errors and a (\mjseqnboot\_size\times hn(k^\ast+m)) matrix

References

Girolimetto, D., Athanasopoulos, G., Di Fonzo, T., & Hyndman, R. J. (2023), Cross-temporal Probabilistic Forecast Reconciliation, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2303.17277")}.

See Also

Other bootstrap: boot_cs(), boot_te()

FoReco documentation built on May 31, 2023, 5:17 p.m.