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)

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.



  1. The underlying model is described here.
  2. Warning: ARMA_CALIBRATE() function is deprecated as of version 1.63: use ARMA_PARAM function instead.
  3. The time series is homogeneous or equally spaced.
  4. The time series may include missing values (e.g. #N/A) at either end.
  5. 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



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