AIRLINE_PARAM - Values of the Model's Parameters

Returns an array of cells for the initial/quick guess of the model's parameters


AIRLINE_PARAM ([x], order, µ, σ, s, θ, θs, return, 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 model mean (i.e., mu) or the long run mean of the differenced time series.
Required. Is the standard deviation of the model's residuals/innovations.
Required. Is the length of seasonality (expressed in terms of lags, where s 1).
Optional. Is the coefficient of the non-seasonal MA component (see model description).
Optional. Is the coefficient of the seasonal MA component (see model description).
Optional. Is an integer switch to select the output array: (1 = Quick Guess (default), 2 = Calibrated, 3 = Std. Errors).
Value Return
1 Quick guess (non-optimal) of parameter values (default).
2 Calibrated (optimal) values for the model's parameters.
3 Standard error of the parameters' values.
Optional. 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. The time series is homogeneous or equally spaced.
  3. The time series may include missing values (e.g., #N/A) at either end.
  4. AIRLINE_PARAM returns an array of the values (or errors) of the model's parameters in the following order:
    1. $\mu$.
    2. $\theta$.
    3. $\Theta$.
    4. $\sigma$.
  5. The AIRLINE_PARAM sets the $\mu$ and $\sigma$ equal to the differenced sample (i.e., $Z_t=(1-L)(1-L^s)Y_t$) average and standard deviation, respectively, and it sets $\theta = 0$ and $\Theta=0$.
  6. The function was added in version 1.63 SHAMROCK.

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