%   
% Written by:
% -- 
% John L. Weatherwax                2009-04-21
% 
% email: wax@alum.mit.edu
% 
% Please send comments and especially bug reports to the
% above email address.
% 
%-----

close all; drawnow; clc; clear; clear functions; 

% calculate the correlation of these residuals:
if( exist('/home/weatherw/Trash/Poularikas/AdaptiveFilteringCode') ) addpath('/home/weatherw/Trash/Poularikas/AdaptiveFilteringCode'); end
addpath( '../Chapter3' ); 

Y_save = load_computer_software_sales(); n = length(Y_save); 

% downsample our data: 
n = length(Y_save)-4; Y = Y_save(1:n); Y_test = Y_save( (n+1):end );

fh = figure; plot( Y, 'x-' ); 
xlabel( 'Time (months)' ); ylabel( 'Monthly Farm Parity Ratios' ); 
saveas(gcf, '../../WriteUp/Graphics/Chapter5/css_plt', 'epsc' ); 

fprintf('s^2(Y)= %10.6f, s^2(diff(Y))= %10.6f, s^2(diff(Y,2))= %10.6f, s^2(diff(Y,3))= %10.6f\n',var(Y),var(diff(Y)),var(diff(Y,2)),var(diff(Y,3)))

% non-statonary ... take first differences and plot: 
YD = diff(Y); nd = length(YD); 
figure; plot( YD, 'x-' ); 
xlabel( 'Time (months)' ); ylabel( 'Montly Farm Parity Ratios' ); 
saveas(gcf, '../../WriteUp/Graphics/Chapter5/css_diff_plt', 'epsc' ); 

r_k = plot_SACF( YD );
title( 'sample autocorrelation function of z_t-z_{t-1}' ); 
saveas(gcf, '../../WriteUp/Graphics/Chapter5/css_diff_z_t_sacf', 'epsc' ); 

phi_kk = plot_SPACF( r_k(2:end), nd ); 
title( 'sample partial autocorrelation function of z_t-z_{t-1}' ); 
saveas( gcf, '../../WriteUp/Graphics/Chapter5/css_diff_spacf', 'epsc' ); 

% determine an approporite ARIMA model ... say MA(1) model 

% calculate the forecasts and 95% prediction intervals for the next 3 months ... done in *.R

% calculate the forecast for the combined sales in the next quarter and a 95 % confidence interval ... done in *.R