ARMA_CALIBRATE - Optimal Values for Model's Parameters

Computes the maximum likelihood estimate (MLE) of the model's parameters.

Syntax

ARMA_CALIBRATE(X, Order, mean, sigma, phi, theta, maxIter)
X
is the univariate time series data (a one-dimensional array of cells (e.g. rows or columns)).
Order
is the time order in the data series (i.e. the first data point's corresponding date (earliest date=1 (default), latest date=0)).
Order Description
1 ascending (the first data point corresponds to the earliest date) (default)
0 descending (the first data point corresponds to the latest date)
mean
is the ARMA model long-run mean (i.e. mu).
sigma
is the standard deviation of the model's residuals/innovations.
phi
are the parameters of the AR(p) component model (starting with the lowest lag).
theta
are the parameters of the MA(q) component model (starting with the lowest lag).
maxIter
is the maximum number of iterations used to calibrate the model. If missing, the default maximum of 100 is assumed.

 Warning

ARMA_CALIBRATE() function is deprecated as of version 1.63: use ARMA_PARAM function instead.

Remarks

  1. The underlying model is described here.
  2. The time series is homogeneous or equally spaced.
  3. The time series may include missing values (e.g. #N/A) at either end.
  4. The maximum likelihood estimation (MLE) is a statistical method for fitting a model to the data and provides estimates for the model's parameters.

 

Files Examples

References

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