decompa.m

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

function [perc,stochc]=decompA(L,beg_i,end_i,N);
%DECOMPA Differencing-smoothing daily data decomposition. 
%	[PERC,STOCHC]=DECOMPA(L,BEG_I,END_I,N) decomposes daily data 
%   L(BEG_I:END_I) into seasonal (deterministic, periodic) component PERC
%   and stochastic component STOCHC using the differencing-smoothing 
%   technique, see [1], Section 2.4.1. (N+1) is the number of weeks used;
%   data vector L is required to include at least (N+1)*7 values preceding
%   BEG_I.
%
%   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

datal = end_i-beg_i+1;
stochc = zeros(datal,1);
perc = zeros(datal,1);
start = beg_i-1;
for i=1:datal
    % seasonal (deterministic, periodic) component
    perc(i) = mean(L(start+i-7:-7:start+i-N*7)) + ...
        mean(L(start+i-1:-1:start+i-7)) - mean(L(start+i-7-1:-1:start+i-(N+1)*7));
    % stochastic component
    stochc(i) = L(start+i)-perc(i);
end;