Joint block bootstrap for generating probabilistic base forecasts that take into account the correlation between different time series (Panagiotelis et al. 2023).
csboot(model_list, boot_size, block_size, seed = NULL)A list with two elements: the seed used to sample the errors and a 3-d array (\(\text{boot\_size}\times n \times \text{block\_size}\)).
A list of all the \(n\) base forecasts models. 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.
Block size of the bootstrap, which is typically equivalent to the forecast horizon.
An integer seed.
Panagiotelis, A., Gamakumara, P., Athanasopoulos, G. and Hyndman, R.J. (2023), Probabilistic forecast reconciliation: Properties, evaluation and score optimisation, European Journal of Operational Research 306(2), 693–706. tools:::Rd_expr_doi("http://dx.doi.org/10.1016/j.ejor.2022.07.040")
Bootstrap samples:
ctboot(),
teboot()
Cross-sectional framework:
csbu(),
cscov(),
cslcc(),
csmo(),
csrec(),
cstd(),
cstools()