/* Note:Your choice is C IDE */
#include "stdio.h"
void main()
{
}/*用小波神经网络来对时间序列进行预测 */
/*%File name : nprogram.m
%Description : This file reads the data from
%its source into their respective matrices prior to
% performing wavelet decomposition.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Clear command screen and variables */
clc;
clear;
/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% ur desired resolution level (Tested: resolution = 2 is best)*/
level = menu('Enter desired resolution level: ', '1',...
'2 (Select this for testing)', '3', '4');
switch level
ca4级成绩 1, resolution = 1;
ca 2, resolution = 2;
ca 3, resolution = 3;
ca 4, resolution = 4;
end
msg = ['Resolution level to be ud is ', num2str(resolution)];
disp(msg);
/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% ur desired amount of data to u */
data = menu('Choo amount of data to u: ', '1 day', '2 days', '3 days', '4 days',...
'5 days', '6 days', '1 week (Select this for testing)');
switch data
ca 1, dataPoints = 48; /*%1 day = 48 points */
ca 2, dataPoints = 96; /* %2 days = 96 points */
ca 3, dataPoints = 144; /*%3 days = 144 points */
ca 4, dataPoints = 192; /*%4 days = 192 points */
ca 5, dataPoints = 240; /* %5 days = 240 points */
ca 6, dataPoints = 288; /* %6 days = 288 points */
ca 7, dataPoints = 336; /*%1 weeks = 336 points */
end
msg = ['No. of data points to be ud is ', num2str(dataPoints)];
disp(msg);
meantodo
/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Menu for data t lection */
lect =谦受益满招损英语翻译 menu('U QLD data of: ', 'Jan02',...
'Feb02', 'Mar02 (Select this for testing)', 'Apr02', 'May02');
switch lect
ca 1, demandFile = 'DATA200601_QLD1';
ca 2, demandFile = 'DATA200602_QLD1';
ca 3, demandFile = 'DATA200603_QLD1';
ca 4, demandFile = 'DATA200604_QLD1';
ca 5, demandFile = 'DATA200605_QLD1';
end
/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Reading the historical DEMAND data into tDemandArray */
lectedDemandFile=[demandFile,'.csv'];
[regionArray, sDateArray, tDemandArray, rrpArray, pTypeArray] ...
= textread(lectedDemandFile, '%s %q %f %f %s', 'headerlines', 1, 'delimiter', ',');
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/*%Display no. of points in the lected time ries demand data */
[demandDataPoints, y] = size(tDemandArray);
msg = ['The no. of points in the lected Demand data is ', num2str(demandDataPoints)];
disp(msg);
/*%Decompo historical demand data signal */
[dD, l] = swtmat(tDemandArray, resolution, 'db2');
approx = dD(resolution, :);
/*%Plot the original demand data signal */
figure (1);
subplot(resolution + 2, 1, 1); plot(tDemandArray(1: dataPoints))
商务英语口语王legend('Demand original');
title('QLD Demand Data Signal');
/*%Plot the approximation demand data signal */
for i = 1 : resolution
subplot(resolution + 2, 1, i + 1); plot(approx(1: dataPoints))
legend('Demand Approximation');
end
/*%After displaying approximation signal, display detail x */
for i = 1: resolution
if( i > 1 )
detail(i, :) = dD(i-1, :)- dD(i, :);
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detail(i, :) = tDemandArray' - dD(1, :);
end
xiaofan
if i == 1
subplot(resolution + 2, 1, resolution - i + 3); plot(detail(i, 1: dataPoints))
legendName = ['Demand Detail ', num2str(i)];
legend(legendName);
el
subplot(resolution + 2, 1, resolution - i +indicate 3); plot(detail(i, 1: dataPoints))
legendName = ['Demand Detail '北大青鸟培训课程, num2str(i)];
legend(legendName);
end
i = i + 1;
end
/*%Normalising approximation demand data */
maxDemand = max(approx'); %Find largest component emperor怎么读
normDemand = approx ./ maxDemand; /*%Right divison */
maxDemandDetail = [ ];
normDemandDetail = [, ];
detail = detail + 4000;
for i = 1: resolution
