Description Usage Arguments Value Note Author(s) References Examples
Autoregressive model incorporating nonlinear regressions of component series.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 
variable 
a numeric vector. 
h 
integer; 1 (default) Number of periods to forecast. 
training.set 
numeric;

seasonal.factor 
logical or integer(s); 
weights 
numeric or 
best.periods 
integer; [2] (default) used in conjunction with 
modulo 
integer(s); NULL (default) Used to find the nearest multiple(s) in the reported seasonal period. 
mod.only 
logical; codeTRUE (default) Limits the number of seasonal periods returned to the specified 
negative.values 
logical; 
method 
options: ("lin", "nonlin", "both"); 
dynamic 
logical; 
plot 
logical; 
seasonal.plot 
logical; 
conf.intervals 
numeric [0, 1]; 
ncores 
integer; value specifying the number of cores to be used in the parallelized procedure. If NULL (default), the number of cores to be used is equal to half the number of cores of the machine  1. 
Returns a vector of forecasts of length (h)
if no conf.intervals
specified. Else, returns a data.table with the forecasts as well as lower and upper confidence intervals per forecast point.
For monthly data series, increased accuracy may be realized from forcing seasonal factors to multiples of 12. For example, if the best periods reported are: {37, 47, 71, 73} use
(seasonal.factor = c(36, 48, 72))
.
(seasonal.factor = FALSE)
can be a very computationally expensive exercise due to the number of seasonal periods detected.
If error encountered when (seasonal.factor = TRUE)
:
"NaNs produced Error in seq.default(length(variable)+1, 1, lag[i]) :
wrong sign in 'by' argument"
use the combination of (seasonal.factor = FALSE, best.periods = 1)
.
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
Viole, F. (2019) "Forecasting Using NNS" https://www.ssrn.com/abstract=3382300
1 2 3 4 5 6 7 8 9 10  ## Nonlinear NNS.ARMA using AirPassengers monthly data and 12 period lag
## Not run:
NNS.ARMA(AirPassengers, h = 45, training.set = 100, seasonal.factor = 12, method = "nonlin")
## Linear NNS.ARMA using AirPassengers monthly data and 12, 24, and 36 period lags
NNS.ARMA(AirPassengers, h = 45, training.set = 120, seasonal.factor = c(12, 24, 36), method = "lin")
## Nonlinear NNS.ARMA using AirPassengers monthly data and 2 best periods lag
NNS.ARMA(AirPassengers, h = 45, training.set = 120, seasonal.factor = FALSE, best.periods = 2)
## End(Not run)

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