1. NETWORK COSTING ANALYSIS (SAMPLE ASSIGNMENT)
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A neural network based dynamic forecasting model for Trend Impact Analysis
Main Function.m
AdvancedTIA()
% rand('state',0); -- In case you want to fix the seed; for testing purposes
NumS=100,000;
NumE=3;
NumY=2;
TMaxMx=xlsread('Time_Max_Impact.xls');
MaxImpMx=xlsread('Max_Impact.xls');
TSSMx=xlsread('Time_SteadyState_Impact.xls');
SSImpMx=xlsread('Steady_State_Impact.xls');
ProbOccMx=[];
ProbOccMx(:,:,1)=xlsread('High_Sev.xls');
ProbOccMx(:,:,2)=xlsread('Medium_Sev.xls');
ProbOccMx(:,:,3)=xlsread('Low_Sev.xls');
TMax=[];
MaxImp=[];
TSS=[];
SSIMP=[];
SrMx=[];
SrMx=ones(NumY*12, NumS);
for s=1:NumS
for e=1:NumE
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2. for y=1:NumY
d=GenerateDegreeSeverity(e,y,ProbOccMx);
if d>0
TMax=TMaxMx(e,d);
MaxImp=MaxImpMx(e,d);
TSS=TSSMx(e,d);
SSImp=SSImpMx(e,d);
m=randint(1,1,[1,12]);
FracChgVect=ComputeFracChgVect(m,y,NumY,MaxImp,SSImp,TMax,TSS);
SrMx(:,s)=SrMx(:,s).*FracChgVect';
end
end
end
end
load TData % The data we used in the illustrated example (paper)
[net,eTrn,eTst] =
NN(timeSeries,numInput,numOutput,hL,lFs,lR,errorGoal,epochs,momentum);
SrMx=GenSrMx(SrMx,net,NumS,NumY);
DrawTIA(NumY,SrMx,timeSeries);
SSUpdate.m
function[out]=SSUpdate(Idx,MaxImp,TMax,SSImp,TSS,NumY)
FracChgVectSS=[];
NumM=NumY*12;
if MaxImp==SSImp
FracChgVectSS(1:min(NumM,Idx+TSS)-(Idx+TMax)+1)=SSImp;
else

3. for i=1:min(NumM,Idx+TSS)-(Idx+TMax)+1
FracChgVectSS(i)=MaxImp-SSImp/TSS*i;
end
end
out=FracChgVectSS;
MaxUpdate.m
function[out]=MaxUpdate(Idx,MaxImp,TMax,NumY)
FracChgVectMax=[];
NumM=NumY*12;
if TMax==0
FracChgVectMax=MaxImp;
else
for i=1:min(NumM,Idx+TMax)-Idx+1
FracChgVectMax(i)=MaxImp/TMax*i;
end
end
out=FracChgVectMax;
GenerateDegreeSeverity.m
function[out]=GenerateDegreeSeverity(e,y,ProbOccMx)
u=rand(1);
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4. d=0;
if u<=ProbOccMx(e,y,1)
d=1;
end
if u<=(ProbOccMx(e,y,2)+ProbOccMx(e,y,1)) &
u>ProbOccMx(e,y,1)
d=2;
end
if
u<=(ProbOccMx(e,y,3)+ProbOccMx(e,y,2)+ProbOccMx(e,y,1))
& u>(ProbOccMx(e,y,2)+ProbOccMx(e,y,1))
d=3;
end
out=d;
ComputeFracChgVect.m
function[out]=ComputeFracChgVect(m,y,NumY,MaxImp,SSImp,TMax,TSS)
FracChgVect=[];
NumM=NumY*12;
Idx=(y-1)*12+m;
FracChgVect(1:Idx)=0;
FracChgVect(Idx:min(NumM,Idx+TMax))=MaxUpdate(Idx,MaxImp,TMax,NumY);
if NumM>Idx+TMax
FracChgVect(Idx+TMax:min(NumM,Idx+TSS))=SSUpdate(Idx,MaxImp,TMax,SSImp,T
SS,NumY);
end
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5. if NumM>Idx+TSS
FracChgVect(Idx+TSS:NumM)=SSImp;
end
out=FracChgVect;
DrawTIA.m
function[out]=DrawTIA(NumY,SrMx,BaseFr)
history=xlsread('Historical_Values.xls')';
med=[];
p5=[];
p95=[];
for i=1:(NumY*12)
med(i)=median(SrMx(i,:));
p95(i)=prctile(SrMx(i,:),95);
p5(i)=prctile(SrMx(i,:),5);
end
generalmat=[];
generalmat(1,:)=[history BaseFr'];
generalmat(2,:)=[history med];
generalmat(3,:)=[history p5];
generalmat(4,:)=[history p95];
plot(generalmat')
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