simp_forec: Combinate the forecasts and prediction intervals by simple...
In xqnwang/fuma: Forecast uncertainty based on model averaging (FUMA)

The combinated forecasts and prediction intervals are calculated by simple average method.

simp_forec(
  dataset,
  level,
  method.num,
  methodname = "comb",
  parallel = FALSE,
  num.cores = 2
)

`dataset`	a list of time series. See details for the required format.
`level`	the confidence level of prediction intervals that are used for combination.
`method.num`	the number of methods used for averaging. Methods in the first `method.num` rows in `ff` will be selected for simple averaging.
`methodname`	string. the name used to name the combined forecasting results.
`parallel`	logical. If `TRUE` then the calculations are conducted in parallel.
`num.cores`	the specified amount of parallel processes to be used if parallel = TRUE.

dataset must be a list with each element having the following format:

n: the number of observations in the time series.
h: the number of required forecasts.
period: interval of the time series. Possible values are "YEARLY", "QUARTERLY", "MONTHLY" & "OTHER".
x: a time series of length n (the historical data).
xx: a time series of length h (the future data).
ff: a matrix with F rows and h columns. Each row contains the forecasts of each method in methods.
lower: a list with each element being the matrix of lower bounds for certain confidence level.
upper: a list with each element being the matrix of upper bounds for certain confidence level.

A list with the elements having the following structure

ff: a matrix with F+1 rows and h columns where the first F rows represent the point forecasts of benchmark methods and the last row represents the point forecasts by model averaging.
lower: a list with one element that contains a matrix. The matrix with F+1 rows and h columns where the first F rows represent the lower bounds of benchmark methods and the last row represents the lower bounds by model averaging.
upper: a list with one element that contains a matrix. The matrix with F+1 rows and h columns where the first F rows represent the upper bounds of benchmark methods and the last row represents the upper bounds by model averaging.