This interface represents a fitting method for estimating φ, θ, μ, σ2 in an ARMA model.
Forecasts an ARMA time series using the innovative algorithm.
Computes the h-step ahead prediction of a causal ARMA model, by the innovative algorithm.
Computes the one-step ahead prediction of a causal ARMA model, by the innovative algorithm.
A univariate ARMA model, Xt, takes this form.
The ARMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables.
This class represents an AR model.
Compute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.
Computes the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model by recursion.
The method Conditional Sum of Squares (CSS) fits an ARIMA model by minimizing the conditional sum of squares.
The linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of AR terms.
This class represents a univariate MA model.
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