Forecasting of Chinese E-Commerce Sales: An Empirical Comparison of ARIMA, Nonlinear Autoregressive Neural Network, and a Combined ARIMA-NARNN Model

Box 1

% solved by NARNN based on matlab

% rawdata is a weekly sales data of chocolate of the JD.COM

rawdata=data

T = tonndata(rawdata,false,false);

%  ‘trainlm’ is usually fastest.

%   ‘trainbr’ takes longer but may be better for challenging problems.

%   ‘trainscg’ uses less memory. NTSTOOL falls back to this in low memory situations.

trainFcn = ‘trainbr’; % Bayesian Regularization

feedbackDelays = 1:3;

hiddenLayerSize = 17;

net = narnet(feedbackDelays,hiddenLayerSize, ‘open’,trainFcn);

net.input.processFcns = ‘removeconstantrows’, ‘mapminmax’;

= preparets(net,,,T);

net.divideFcn = ‘dividerand’; % Divide data randomly

net.divideMode = ‘time’; % Divide up every value

net.divideParam.trainRatio = 70/100;

net.divideParam.valRatio = 15/100;

net.divideParam.testRatio = 15/100;

net.performFcn = ‘mse’;

net.plotFcns = ‘plotperform’, ‘plottrainstate’, ‘plotresponse’,...

‘ploterrcorr’, ‘plotinerrcorr’;

= train(net,x,t,xi,ai);

y = net(x,xi,ai);

e = gsubtract(t,y);

performance = perform(net,t,y)

trainTargets = gmultiply(t,tr.trainMask);

valTargets = gmultiply(t,tr.valMask);

testTargets = gmultiply(t,tr.testMask);

trainPerformance = perform(net,trainTargets,y)

valPerformance = perform(net,valTargets,y)

testPerformance = perform(net,testTargets,y)

view(net)

% Plots

% Uncomment these lines to enable various plots.

%figure, plotperform(tr)

%figure, plottrainstate(tr)

%figure, plotresponse(t,y)

%figure, ploterrcorr(e)

%figure, plotinerrcorr(x,e)

netc = closeloop(net);

= preparets(netc,,,T);

yc = netc(xc,xic,aic);

perfc = perform(net,tc,yc)

= preparets(net,,,T);

= net(x1,xio,aio);

= closeloop(net,xfo,afo);

= netc(cell(0,5),xic,aic);

nets = removedelay(net);

= preparets(nets,,,T);

ys = nets(xs,xis,ais);

stepAheadPerformance = perform(net,ts,ys)

genFunction(net, ‘myNeuralNetworkFunction’);

y = myNeuralNetworkFunction(x,xi,ai);

end

if (false)

genFunction(net, ‘myNeuralNetworkFunction’, ‘MatrixOnly’, ‘yes’);

x1 = cell2mat(x(1,:));

xi1 = cell2mat(xi(1,:));

y = myNeuralNetworkFunction(x1,xi1);

end

if (false)

gensim(net);

end