predict

forecast

See the following help files:<ul>
<li><code>predict.marssMLE()</code> Predict and forecast.</li>
<li><code>forecast.marssMLE()</code> Forecast. Use <code>predict.marssMLE()</code> to call with argument <code>h</code>.</li>
<li><code>plot.marssPredict()</code> Plot a prediction or forecast.</li>
<li><code>autoplot.marssPredict()</code> Plot a prediction or forecast using ggplot2 package.</li>
<li><code>print.marssPredict()</code> Print prediction or forecast. If <code>h!=0</code>, i.e. forecast, only the forecast is printed but the <code>marssPredict</code> object (in <code>pred</code>) has all the historical time steps also.</li>
</ul>

The MARSS package provides maximum-likelihood parameter
estimation for constrained and unconstrained linear multivariate autoregressive
state-space (MARSS) models, including partially deterministic models. MARSS models are a class
of dynamic linear model (DLM) and vector autoregressive model (VAR)
model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and 'TMB'
(using the 'marssTMB' companion package). Functions are provided for parametric and
innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including
bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all
conditional residual types. See the user guide for examples of dynamic factor analysis,
dynamic linear models, outlier and shock detection, and multivariate AR-p models.
Online workshops (lectures, eBook, and computer labs)
at <https://atsa-es.github.io/>.

predict: predict and forecast MARSS MLE objects

Description

Arguments