How to forecast using ANN

Question

HI, please help me to forecast feature groundwater level using feed forward neural network with LM algorithm, i developed the feed forward neural network with LM algorithm with ANN architecture 3-5-1, my model is as follows, but i want to use this model for forecasting at least 3 to 6 point ahead, can any one please help me,

  clear all;
  
  clc;
  
  load dannlm.txt;
  
  t=dannlm(:,1);
  
  rain=dannlm(:,2);
  
  et=dannlm(:,3);
  
  gwl=dannlm(:,4);
  
  %[ACF, Lags, Bounds] = autocorr(gwl, [], 2);
  
  %[PACF, Lags, Bounds] = parcorr(gwl, [], 2);
  
  %[CCF, Lags, Bounds] = crosscorr(gwl,rain);
  
  
  gwlmin=min(gwl);
  
  gwlmax=max(gwl);
  
  rain=(rain-min(rain))/(max(rain)-min(rain));
  
  et=(et-min(et))/(max(et)-min(et));
  
  gwl=(gwl-min(gwl))/(max(gwl)-min(gwl));
  
  %*****Data Preparation****
  
  for t=5:195
  
      Data(t-4,:)=[rain(t-4) et(t-1) gwl(t-1) gwl(t)];
  
  end
  
  %Define Input patter for Training and Validation
  
  INPTR=Data(1:150, 1:3);
  
  TARTR=Data(1:150,4);
  
  net=newff([0 1; 0 1; 0 1], [5 1], {'logsig', 'purelin'}, 'trainlm');
  
  net.trainParam.epochs=500;
  
  net.trainParam.goal=0.0001;
  
  net.performFcn='mse';
  
  net=init(net);
  
  net=train(net, INPTR', TARTR');
  
  a=sim(net, INPTR');
  
  z=[a' TARTR];
  
  INPVAL=Data(151:191, 1:3);
  
  TARVAL=Data(151:191,4);
  
  y=sim(net, INPVAL');
  
  zv=[y' TARVAL];
  
  %Converting back to Original Flow of gwl Validation
  
  zv=zv*(gwlmax-gwlmin)+gwlmin;
  
  save val1.txt zv -ascii;
  
  z=z*(gwlmax-gwlmin)+gwlmin;
  
  save cal1.txt z -ascii;
  
  CORR_CAL=corrcoef(z)
  
  CORR_VAL=corrcoef(zv)
  
  COM_CAL=z(:,1);
  
  OBS_CAL=z(:,2);
  
  COM_VAL=zv(:,1);
  
  OBS_VAL=zv(:,2);
  
  Eff_ANN_CAL=1-(sumsqr(OBS_CAL-COM_CAL)/sumsqr(OBS_CAL-mean(OBS_CAL)))
  
  Eff_ANN_VAL=1-(sumsqr(OBS_VAL-COM_VAL)/sumsqr(OBS_VAL-mean(OBS_VAL)))
  
  RMSE_ANN_CAL=sqrt(sumsqr(OBS_CAL-COM_CAL)/length(OBS_CAL))
  
  RMSE_ANN_VAL=sqrt(sumsqr(OBS_VAL-COM_VAL)/length(OBS_VAL))
  
  Ex_Var_ANN_CAL=sqrt(sumsqr(COM_CAL-mean(OBS_CAL))/sumsqr(OBS_CAL-mean(OBS_CAL)))
  
  %ERROR_CAL=100*(OBS_CAL-COM_CAL)/OBS_CAL;
  
  %ERROR_VAL=100*(OBS_VAL-COM_VAL)/OBS_VAL;
  
  ANN_PEAK_CAL=(1-max(COM_CAL)/max(OBS_CAL))*100
  
  ANN_PEAK_VAL=(1-max(COM_VAL)/max(OBS_VAL))*100

Greg Heath · Accepted Answer

help narxnet
 doc narxnet

 net = narxnet(3:6,3:6,5);

===================================================================

% ===>GEH1: WHAT DO THE NAMES DANNLM AND ET STAND FOR?

% ===> GEH2: AUTOCORR(gwl,...), CROSSCORR(GWL,RAIN,...) AND
% CROSSCORR(GWL,ET,...) CAN BE USED TO FIND THE SIGNIFICANT DELAYS.

 for t=5:195
    Data(t-4,:)=[rain(t-4) et(t-1) gwl(t-1) gwl(t)];
 end

% ===>GEH3: What is the rationale for this combination?

% ===> GEH4: NEWFF has been obsolete for at least 5 years. Regardless, this is a time series problem which is more easily solved using NARXNET or it's obsolete predecessor.

 z=[a' TARTR];

% ===>GEH5: Why not compute the error mse(a'-TARTR) ???

 INPVAL=Data(151:191, 1:3);
 TARVAL=Data(151:191,4);

% ===> GEH6: This is TEST data, NOT VALIDATION DATA! VALIDATION DATA IS USED TO TUNE PARAMETERS. TEST DATA IS USED TO OBTAIN UNBIASED ESTIMATES OF PERFORMANCE

% THAT IS AS FAR AS I WENT. AFTER CALCULATING 

 NMSE = mse(error)/mean(var(target',1) and/or 
 R2   = 1-NMSE

%SUCCESS OR FAILURE IS DETERMINED

Hope this helps.

*Thank you for formally accepting my answer*

Greg

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