estimate: Model Estimation

In tsmodels/tsvets: Vector ETS Models

Model Estimation

Description

Estimates a model given a specification object using maximum likelihood.

Usage

## S3 method for class 'tsvets.spec'
estimate(
  object,
  solver = "nlminb",
  control = list(trace = 0, iter.max = 200, eval.max = 1000),
  autodiff = FALSE,
  ...
)

Arguments

object	an object of class “tsvets.spec”.
solver	currently “L-BFGS-B” from “optim”, “nlminb”, “solnp”, “gosolnp” and “nloptr” bound constrained solvers are supported. For the case of autodiff, only “optim” and “nlminb” are supported, with the latter allowing for the use of the hessian.
control	solver control parameters.
autodiff	whether to use automatic differentiation for estimation. This makes use of the tsvetsad package.
...	additional arguments to the “gosolnp” solver. In the case when autodiff is TRUE, then the only other arguments are “use_hessian” (logical) which works with the nlminb solver and “silent” (logical) which optionally turns on messages from the TMB package (default is TRUE).

Details

Minimization of the negative of the log likelihood function of the vector Additive ETS model with a soft barrier based on the stability constraint of the model.

Value

An object of class “tsvets.estimate”

References

Athanasopoulos, G and de Silva, A. (2012), Multivariate Exponential Smoothing for Forecasting Tourist Arrivals, Journal of Travel Research 51(5) 640–-652.
de Silva, A., R. Hyndman, and R. D. Snyder. (2010).The Vector Innovations Structural Time Series Framework: A Simple Approach to Multivariate Forecasting, Statistical Modelling (10) 353–74.

tsmodels/tsvets documentation built on June 13, 2022, 2:14 p.m.