ctboot function

Cross-temporal joint block bootstrap

Joint block bootstrap for generating probabilistic base forecasts that take into account the correlation between variables at different temporal aggregation orders (Girolimetto et al. 2023).

ctboot(model_list, boot_size, agg_order, block_size = 1, seed = NULL)

Arguments

• model_list: A list of $n$ elements, one for each cross-sectional series. Each elements is a list with the $(k^\ast+m)$ base forecasts models ordered from the lowest frequency (most temporally aggregated) to the highest frequency. 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.
• agg_order: Highest available sampling frequency per seasonal cycle (max. order of temporal aggregation, $m$), or a vector representing a subset of $p$ factors of $m$.
• block_size: Block size of the bootstrap, which is typically equivalent to the forecast horizon for the most temporally aggregated series.
• seed: An integer seed.

Returns

A list with two elements: the seed used to sample the errors and a ($\text{boot\_size}\times n(k^\ast+m)\text{block\_size}$) matrix.

References

Girolimetto, D., Athanasopoulos, G., Di Fonzo, T. and Hyndman, R.J. (2023), Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues. International Journal of Forecasting, in press. tools:::Rd_expr_doi("10.1016/j.ijforecast.2023.10.003")

See Also

Bootstrap samples: csboot(), teboot()

Cross-temporal framework: ctbu(), ctcov(), ctlcc(), ctmo(), ctrec(), cttd(), cttools(), iterec(), tcsrec()