mfe_loadf_preda.m

Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach" by Rafa&#322 Weron, p

function F=mfe_loadf_predA(perc,stochc,cal_period,for_period,model_size,X)
%MFE_LOADF_PREDA Auxiliary routine for MFE_LOADF.
%	F=MFE_LOADF_PREDA(...) returns a vector of Model B day-ahead 
%   forecasts of the daily time series L.
%
%   Input data:
%       PERC - seasonal (deterministic) component for the full period,
%       STOCHC - stochastic component for the full period,
%       CAL_PERIOD - length of the calibration period,
%       FOR_PERIOD - length of the forecast period,
%       MODEL_SIZE - ARMA(X) model size; type "help armax" at the command
%           line for format description,
%       X - exogenous variable, 
%
%   Reference(s):
%   [1] R.Weron (2007) "Modeling and Forecasting Electricity Loads and 
%   Prices: A Statistical Approach", Wiley, Chichester.

%   Written by Adam Misiorek and Rafal Weron (2006.09.22)
%   Copyright (c) 2006 by Rafal Weron

y = zeros(for_period,1);
F = zeros(for_period,1);
if size(model_size,2)==2
    disp(['ARMA [',num2str(model_size),']'])
    disp(['Processing ' num2str(for_period) ' data points ...'])
    for t = 1:for_period,
        % calibrate ARMA model to the stochastic component
        M66 = armax(stochc(t:cal_period+t-1),model_size); 
        % make a day-ahead prediction
        y66 = predict(stochc(t:cal_period+t),M66);  
        y(t) = y66(end);
        F(t) = perc(cal_period+t)+y(t);
    end
else 
    disp(['ARMAX [',num2str(model_size),']'])
    disp(['Processing ' num2str(for_period) ' data points ...'])
    for t = 1:for_period,
        % calibrate ARMAX model to the stochastic component
        M66 = armax([stochc(t:cal_period+t-1-1) X(t:cal_period+t-1-1)],model_size); 
        % make a day-ahead prediction
        y66 = predict([stochc(t:cal_period+t-1) X(t:cal_period+t-1)],M66); 
        y(t) = y66(end);
        F(t) = perc(cal_period+t)+y(t);
    end
end