Joint block bootstrap for generating probabilistic base forecasts that take into account the correlation between different time series (Panagiotelis et al. 2023).
boot_cs(fit, boot_size, h, seed = NULL)A list of \(n\) base forecast models. 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.
Block size of the bootstrap, which is typically equivalent to the forecast horizon.
An integer seed.
A list with two elements: the seed used to sample the errors and a 3-d array (\(boot\_size\times n \times h\))
Panagiotelis, A., Gamakumara, P., Athanasopoulos, G. & Hyndman, R. J. (2023), Probabilistic forecast reconciliation: Properties, evaluation and score optimisation, European Journal of Operational Research 306(2), 693–706.