Forecast Averaging

Averaging schemes

To exploit the strong predictive performance of combining predictions from different forecasting models, PostForecasts.jl provides averaging schemes for both point and probabilistic forecasts:

PostForecasts.average — Function

average(pf; agg::Symbol=:mean)

Average the pool of point pred from pf. Return PointForecasts containing averaged forecasts, keyword argument agg specifies whether to average using simple mean (:mean) or median (:median).

Methods

average(pf::PointForecasts) to average the pool of forecasts in pf
average(pfs::AbstractVector{<:PointForecasts} to average all individual forecasts from every PointForecasts in pfs
calling with multiple PointForecasts objects as consecutive arguments, e.g. average(pf1, pf2, pf3), is equivalent to average([pf1, pf2, pf3])

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PostForecasts.qaverage — Function

qaverage(qfs::AbstractVector{<:QuantForecasts})

Average probabilistic predictions from qfs by averaging the quantiles.

The function qaverage can also be called by passing QuantForecasts objects as consecutive arguments, e.g. qaverage(qf1, qf2, qf3) is equivalent to qaverage([qf1, qf2, qf3]).

Return QuantForecasts containing quantile predictions at the same quantile levels as QuantForecasts in qfs.

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PostForecasts.paverage — Function

paverage(qfs::AbstractVector{<:QuantForecasts}[; quantiles])

Average probabilistic predictions from qfs by averaging the distributions across probability.

Return QuantForecasts containing predictions of specified quantiles:

quantiles::AbstractVector{<:AbstractFloat}: vector of probabilities
quantiles::AbstractFloat: a single probability value
quantiles::Integer: number of equidistant probability values (e.g. 99 for percentiles).

The function paverage can also be called by passing QuantForecasts objects as consecutive arguments, e.g. paverage(qf1, qf2, qf3) is equivalent to paverage([qf1, qf2, qf3]).

If quantiles argument is not provided, the function will default to the quantiles of the first QuantForecasts in qfs.

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