PTSmodel.m

function m = PTSmodel(y, s, varargin)
%     Run PTS general univariate MSOE models
% 
%     Inputs:
%         y:  A time series to forecast (it may be either a numpy vector or a Panda time series object).
%             This is the only input required. If a vector, the additional input 's' should be supplied
%             compulsorily (see below).
%         s: Seasonal period of time series (1 for annual, 4 for quarterly, ...)
%         u:  A matrix of input time series. If the output wanted to be forecast, matrix 'u' should
%             contain future values for inputs.
%         model:  The model to estimate. It is a single string indicating the type of model for each
%                 component with one or two letters:
%                 - Error: ? / N / A
%                 - Trend: ? / N / A / Ad / L
%                 - Seasonal: ? / N / A / D (trigonometric with different variances)
%         h: Forecast horizon. If the model includes inputs, 'h' is not used; the length of 'u' is used instead.
%         criterion: Information criterion for identification ("aic", "bic", or "aicc").
%         lambda: Box-Cox lambda parameter (None: estimate)
%         armaIdent: Check for ARMA models for the error component (True / False).
%         verbose: Intermediate estimation output (True / False)
% 
%     Output:
%         An object of class 'PTS'. It is a structure with fields including all the inputs and the fields
%         listed below as outputs. All the functions in this package fill in part of the fields of any 'PTS'
%         object as specified in what follows (function 'PTS' fills in all of them at once):
% 
%         After running 'PTSmodel':
%         - p0: Initial values for parameter search
%         - p: Estimated parameters
%         - lambdaBoxCox: Estimated Box-Cox lambda parameter
%         - v: Estimated innovations (white noise in correctly specified models)
%         - yFor: Forecasted values of output
%         - yForV: Variance of forecasted values of output
% 
%         After running 'object.validate':
%         - table: Estimation and validation table
% 
%         After running 'object.components':
%         - comp: Estimated components in matrix form
% 
%     See Also:
%         PTS, UCmodel, UC, ETS, ETSmodel
% 
%     Author: Diego J. Pedregal
    m = PTSsetup(y, s, varargin{:});
    m = PTSestim(m);
end