CoViD19 Meta heuristic optimization based forecast method on time dependent bootstrapped data

Livio Fenga; Carlo Del Castello

In more details, while in the classic bootstrap an ensemble Ω represents the population of reference the observed time series is drawn from, in MEB a large number of ensembles (subsets), say {ω 1 , . . . , ω N } becomes the elements belonging to Ω, each of them containing a large number of replicates {x 1 , . . . , x J }. Perhaps, the most important characteristic of the MEB algorithm is that its design guarantees the inference process to satisfy the ergodic theorem. Formally, denoting by the symbol | · | the cardinality function (counting function) of a given ensemble of time series {x t ∈ ω i ; i = 1, . . . , N }, the MEB procedure generates a set of disjoint subsets Ω N ≡ ω 1 ∩ ω 1 · · · ∩ ω N Page 2 of 10 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.