NNS.ARMA.optim  R Documentation 
Wrapper function for optimizing any combination of a given seasonal.factor
vector in NNS.ARMA. Minimum sum of squared errors (forecastactual) is used to determine optimum across all NNS.ARMA methods.
NNS.ARMA.optim( variable, h = NULL, training.set = NULL, seasonal.factor, negative.values = FALSE, obj.fn = expression(cor(predicted, actual, method = "spearman")/sum((predicted  actual)^2)), objective = "max", linear.approximation = TRUE, lin.only = FALSE, conf.intervals = NULL, print.trace = TRUE )
variable 
a numeric vector. 
h 
integer; 
training.set 
integer; 
seasonal.factor 
integers; Multiple frequency integers considered for NNS.ARMA model, i.e. 
negative.values 
logical; 
obj.fn 
expression;

objective 
options: ("min", "max") 
linear.approximation 
logical; 
lin.only 
logical; 
conf.intervals 
numeric [0, 1]; 
print.trace 
logical; 
Returns a list containing:
$period
a vector of optimal seasonal periods
$weights
the optimal weights of each seasonal period between an equal weight or NULL weighting
$obj.fn
the objective function value
$method
the method identifying which NNS.ARMA method was used.
$shrink
whether to use the shrink
parameter in NNS.ARMA.
$bias.shift
a numerical result of the overall bias of the optimum objective function result. To be added to the final result when using the NNS.ARMA with the derived parameters.
$errors
a vector of model errors from internal calibration.
$results
a vector of length h
.
$lower.conf.int
a vector of lower confidence intervals per forecast point.
$upper.conf.int
a vector of upper confidence intervals per forecast point.
Typically, (training.set = length(variable)  2 * length(forecast horizon))
is used for optimization. Smaller samples would use (training.set = length(variable)  length(forecast horizon))
in order to preserve information.
The number of combinations will grow prohibitively large, they should be kept as small as possible. seasonal.factor
containing an element too large will result in an error. Please reduce the maximum seasonal.factor
.
If variable cannot logically assume negative values, then the $bias.shift
must be limited to 0 via a pmax(0,...)
call.
Fred Viole, OVVO Financial Systems
Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" https://www.amazon.com/dp/1490523995/ref=cm_sw_su_dp
## Nonlinear NNS.ARMA period optimization using 2 yearly lags on AirPassengers monthly data ## Not run: nns.optims < NNS.ARMA.optim(AirPassengers[1:132], training.set = 120, seasonal.factor = seq(12, 24, 6)) ## To use optimal parameters in NNS.ARMA to predict 12 periods insample. ## Note the {$bias.shift} usage in the {NNS.ARMA} function: nns.estimates < NNS.ARMA(AirPassengers, h = 12, training.set = 132, seasonal.factor = nns.optims$periods, method = nns.optims$method) + nns.optims$bias.shift ## If variable cannot logically assume negative values nns.estimates < pmax(0, nns.estimates) ## To predict out of sample using best parameters: NNS.ARMA.optim(AirPassengers[1:132], h = 12, seasonal.factor = seq(12, 24, 6)) ## End(Not run)
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