simulateHoltMA: simulateHoltMA

In olshena/COVIDNearTerm: Near Term Prediction of COVID-Related Population Metrics

simulateHoltMA

Description

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Usage

simulateHoltMA(
  dat,
  pdays,
  nsim,
  level = 0.95,
  bootstrap,
  ma,
  smooth_type = "s",
  model_type = "AAN",
  seed = NULL
)

Arguments

dat	One-dimensional vector of interest like test positivity
pdays	How far in the future to forecast
nsim	Number of simulations
level	Prediction level, not used but need for existing code
bootstrap	Whether to resample errors (TRUE) or use Gaussian errors (FALSE)
ma	Width of moving average window in days
smooth_type	Type of smoothing used by movavg, see movavg documentation for detials
model_type	From the ets documentation: Usually a three-character string identifying method using the framework terminology of Hyndman et al. (2002) and Hyndman et al. (2008). The first letter denotes the error type ("A", "M" or "Z"); the second letter denotes the trend type ("N","A","M" or "Z"); and the third letter denotes the season type ("N","A","M" or "Z"). In all cases, "N"=none, "A"=additive, "M"=multiplicative and "Z"=automatically selected. So, for example, "ANN" is simple exponential smoothing with additive errors, "MAM" is multiplicative Holt-Winters' method with multiplicative errors, and so on.
seed	Random seed to use

Value

A list with the following components:

paths is raw matrix of paths including training data

paths_ma is moving average matrix of paths

smoothed_training is moving average of training data

max_predictions is maximum for each row of paths_ma

olshena/COVIDNearTerm documentation built on May 27, 2023, 1:23 p.m.