NNS.ARMA.optim: NNS ARMA Optimizer

Description Usage Arguments Value Note Author(s) References Examples

View source: R/ARMA_optim.R

Description

Wrapper function for optimizing any combination of a given seasonal.factor vector in NNS.ARMA. Minimum sum of squared errors (forecast-actual) is used to determine optimum across all NNS.ARMA methods.

Usage

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NNS.ARMA.optim(variable, training.set, seasonal.factor, method = "seq",
  negative.values = FALSE)

Arguments

variable

a numeric vector.

training.set

numeric; NULL (defualt) Sets the number of variable observations

seasonal.factor

integers; Multiple frequency integers considered for NNS.ARMA model, i.e. (seasonal.factor = c(12, 24, 36))

method

options: ("comb", "seq"); "comb" Tries all combinations of "seasonal.factor" provided, while "seq" (default) tests each by adding element to previous iteration of "seasonal.factor".

negative.values

logical; FALSE (default) If the variable can be negative, set to (negative.values = TRUE).

Value

Returns a list containing a vector of optimal seasonal periods $period, the minimum SSE value $SSE, and the $method identifying which NNS.ARMA method was used.

Note

The number of combinations will grow prohibitively large, they should be kept to a minimum when (method = "comb").

seasonal.factor containing an element too large will result in an error. Please reduce the maximum seasonal.factor.

Author(s)

Fred Viole, OVVO Financial Systems

References

Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" http://amzn.com/1490523995

Examples

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## Nonlinear NNS.ARMA period optimization using 5 yearly lags on AirPassengers monthly data
## Not run: 
nns.optims <- NNS.ARMA.optim(AirPassengers, training.set = 132, seasonal.factor = seq(12, 60, 12))

## End(Not run)

## Then use optimal parameters in NNS.ARMA
## Not run: 
NNS.ARMA(AirPassengers, training.set=132, seasonal.factor = nns.optims$periods,
method = nns.optims$method)

## End(Not run)

NNS documentation built on April 15, 2019, 5:05 p.m.