ARMA_CALIBRATE

X
is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)).

Order
is the time order of the data series (i.e. whether the first data point corresponds to the earliest or latest date (earliest date=1 (default), latest date=0)).

Model
is the ARMA model representation array (a one dimensional array of cells (e.g. rows or columns)) (see ARMA function).

Mask
is an array of 0's and 1's to specify which parameters to calibrate for. If missing, all parameters are included in the calibration.

Method
is the calibration/fitting method (1=MLE, 2=Bayesian). If missing, maximum likelihood estimate (MLE) is assumed.

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. 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.

• Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0-691-04289-6
• Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740

• Wikipedia - Likelihood function
• Wikipedia - Likelihood principle
• Wikipedia - Autoregressive moving average model