csboot function

Cross-sectional joint block bootstrap

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)

Arguments

• model_list: 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 list("forecast"), with the following mandatory parameters: object, innov, future, and nsim.

• boot_size: The number of bootstrap replicates.

• block_size: Block size of the bootstrap, which is typically equivalent to the forecast horizon.

• seed: An integer seed.

Returns

A list with two elements: the seed used to sample the errors and a 3-d array ($\text{boot\_size}\times n \times \text{block\_size}$).

References

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")

See Also

Bootstrap samples: ctboot(), teboot()

Cross-sectional framework: csbu(), cscov(), cslcc(), csmo(), csrec(), cstd(), cstools()