ARMA_CALIBRATE - Optimal Values for Model's Parameters

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


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


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


  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.


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