- orders
Order of the model. Specified as vector of number of states
with different lags. For example, orders=c(1,1)
means that there are
two states: one of the first lag type, the second of the second type.
- lags
Defines lags for the corresponding orders. If, for example,
orders=c(1,1)
and lags are defined as lags=c(1,12)
, then the
model will have two states: the first will have lag 1 and the second will
have lag 12. The length of lags
must correspond to the length of
orders
.
- obs
Number of observations in each generated time series.
- nsim
Number of series to generate (number of simulations to do).
- frequency
Frequency of generated data. In cases of seasonal models
must be greater than 1.
- measurement
Measurement vector \(w\). If NULL
, then
estimated.
- transition
Transition matrix \(F\). Can be provided as a vector.
Matrix will be formed using the default matrix(transition,nc,nc)
,
where nc
is the number of components in state vector. If NULL
,
then estimated.
- persistence
Persistence vector \(g\), containing smoothing
parameters. If NULL
, then estimated.
- initial
Vector of initial values for state matrix. If NULL
,
then generated using advanced, sophisticated technique - uniform
distribution.
- randomizer
Type of random number generator function used for error
term. Defaults are: rnorm
, rt
, rlaplace
and rs
.
rlnorm
should be used for multiplicative models (e.g. ETS(M,N,N)).
But any function from Distributions will do the trick if the
appropriate parameters are passed. For example rpois
with
lambda=2
can be used as well, but might result in weird values.
- probability
Probability of occurrence, used for intermittent data
generation. This can be a vector, implying that probability varies in time
(in TSB or Croston style).
- ...
Additional parameters passed to the chosen randomizer. All the
parameters should be passed in the order they are used in chosen randomizer.
For example, passing just sd=0.5
to rnorm
function will lead
to the call rnorm(obs, mean=0.5, sd=1)
.