ARMA_CALIBRATE - Optimal Values for Model's Parameters

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


ARMA_CALIBRATE ([x], order, µ, σ, [φ], [θ], maxiter)

Required. Is the univariate time series data (a one-dimensional array of cells (e.g., rows or columns)).
Optional. Is the time order in the data series (i.e., the first data point's corresponding date (earliest date = 1 (default), latest date = 0)).
Value Order
1 Ascending (the first data point corresponds to the earliest date) (default).
0 Descending (the first data point corresponds to the latest date).
Optional. Is the ARMA model long-run mean (i.e., mu). If missing, the process mean is assumed to be zero.
Required. Is the standard deviation value of the model's residuals/innovations.
Optional. Are the parameters of the AR(p) component model: [φ1, φ2 … φp] (starting with the lowest lag).
Optional. Are the parameters of the MA(q) component model: [θ1, θ2 … θq] (starting with the lowest lag).
Optional. 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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