How can i fix this error?

Hi everyone, I'm using the following script which was made by another person for the prediction of various types of time series. In my case I am trying to use it for the Reactive Power prediction, but I am presented with the following error which I cannot solve. How can I solve this problem?

I am attaching the script

Many thanks in advance to who helps me.

clc; clear; close all;
%% ---------------------------- init Variabels ----------------------------

opt.Delays = [1 2 3 4 5 6 7 8 9 10 12 16 20];
opt.dataPreprocessMode  = 'Data Standardization'; % 'None' 'Data Standardization' 'Data Normalization'
opt.learningMethod      = 'LSTM';                 % 'MLP' 'LSTM'    
opt.trPercentage        = 0.8;                    %  divide data into Test  and Train dataset

% ------------- BILSTM parameters 
opt.NumOfHiddenLayers = 2;                        %  number of (bi)LSTM layers

opt.NumOfUnitsInFirstlayer  = 100;                %  number of (bi)LSTM units in the first  layer
opt.NumOfUnitsInSecondlayer = 100;                %  number of (bi)LSTM units in the second layer
opt.NumOfUnitsInThirdlayer  = 75;                 %  number of (bi)LSTM units in the third  layer
opt.NumOfUnitsInFourthlayer = 75;                 %  number of (bi)LSTM units in the forth  layer

opt.isUseBiLSTMLayer  = true;                    % if it is true the layer turn to the Bidirectional-LSTM and if it is false it will turn the units to the simple LSTM
opt.isUseDropoutLayer = true;                    % dropout layer avoid of bieng overfit
opt.DropoutValue      = 0.5;

opt.maxEpochs     = 200;                         % maximum number of training Epoch in bi-LSTM.
opt.miniBatchSize = 64;                          % minimum batch size in bi-LSTM .
opt.executionEnvironment = 'gpu';                % 'cpu' 'gpu' 'auto'
opt.LR                   = 'adam';               % 'sgdm' 'rmsprop' 'adam'
opt.trainingProgress     = 'training-progress';  % 'training-progress' 'none'.

% MLP parameters
opt.NumOfFeedForwardLeyars = 2;

opt.NumOfNeuronsInFirstlayer  = 15;         %  number of neurons in the first  layer
opt.NumOfNeuronsInSecondlayer = 15;         %  number of neurons in the second layer
opt.NumOfNeuronsInThirdlayer  = 10;         %  number of neurons in the third  layer

opt.trainFcn = 'trainbr';                   % 'trainlm' 'trainscg' 'traincgf' 'trainbr'
opt.maxItrations = 100;                     % maximum number of training itration.
opt.showWindow             = true;          % display training window.
opt.showCommandLine        = true;          % display training process on workspace.

opt.isSavePredictedData    = true;         %  save output prediction on an excel file

%% --------------- load Data
data = loadData(opt);
if ~data.isDataRead
    return;
end
%% --------------- Train Network
[opt,data] = TrainData(opt,data);

%% --------------- Evaluate Data 
[opt,data] = EvaluationData(opt,data);


%% ---------------------------- Local Functions ---------------------------
function data = loadData(opt)
 [chosenfile,chosendirectory] = uigetfile({'*.xlsx';'*.csv'},...
                       'Select Excel time series Data sets','data.xlsx');
   filePath = [chosendirectory chosenfile];
    if filePath ~= 0 
        data.DataFileName = chosenfile;
        data.CompleteData = readtable(filePath); 
        if size(data.CompleteData,2)>1
             warning('Input data should be an excel file with only one column!'); 
             disp('Operation Failed... '); pause(.9);
             disp('Reloading data. ');     pause(.9);
             data.x = [];
             data.isDataRead = false;
             return;
        end
        data.seriesdataHeder = data.CompleteData.Properties.VariableNames(1,:);
        data.seriesdata = table2array(data.CompleteData(:,:));
        disp('Input data successfully read.');
        data.isDataRead = true;
        data.seriesdata = PreInput(data.seriesdata);
        
        figure('Name','InputData','NumberTitle','off');
        plot(data.seriesdata);
        title({['Mean = ' num2str(mean(data.seriesdata)) ', STD = ' num2str(std(data.seriesdata)) ];});
        if strcmpi(opt.dataPreprocessMode,'None')
            data.x = data.seriesdata;
        elseif strcmpi(opt.dataPreprocessMode,'Data Normalization')
            data.x = DataNormalization(data.seriesdata);
            figure('Name','NormilizedInputData','NumberTitle','off');
            plot(data.x); grid minor;
            title({['Mean = ' num2str(mean(data.x)) ', STD = ' num2str(std(data.x)) ];});
        elseif strcmpi(opt.dataPreprocessMode,'Data Standardization')
            data.x = DataStandardization(data.seriesdata);
            figure('Name','NormilizedInputData','NumberTitle','off');
            plot(data.x); grid minor;
            title({['Mean = ' num2str(mean(data.x)) ', STD = ' num2str(std(data.x)) ];});
        end
        
    else 
        warning(['In order to train network, please load data.' ...
        'Input data should be an excel file with only one column!']);    
        disp('Operation Cancel.'); 
        data.isDataRead = false;
    end
end
function data = PreInput(data)
    if iscell(data)
        for i=1:size(data,1)
            for j=1:size(data,2)
                if strcmpi(data{i,j},'#NULL!')
                    tempVars(i,j) = NaN; %#ok
                else
                    tempVars(i,j) = str2num(data{i,j});   %#ok
                end
            end
        end
        data = tempVars;
    end

end
function  vars = DataStandardization(data)
    for i=1:size(data,2)
        x.mu(1,i)   = mean(data(:,i),'omitnan');
        x.sig(1,i)  = std (data(:,i),'omitnan');
        vars(:,i) = (data(:,i) - x.mu(1,i))./ x.sig(1,i);
    end
end
function vars = DataNormalization(data)
    for i=1:size(data,2)
        vars(:,i) = (data(:,i) -min(data(:,i)))./ (max(data(:,i))-min(data(:,i)));
    end
end
% --------------- Train Network ---------------
% ---------------------------------------------
function [opt,data] = TrainData(opt,data)
% Bi-LSTM parameters
if opt.NumOfHiddenLayers ==1
    opt.numHiddenUnits1 = opt.NumOfUnitsInFirstlayer;   
elseif opt.NumOfHiddenLayers ==2
    opt.numHiddenUnits1 = opt.NumOfUnitsInFirstlayer; 
    opt.numHiddenUnits2 = opt.NumOfUnitsInSecondlayer;    
elseif opt.NumOfHiddenLayers ==3
    opt.numHiddenUnits1 = opt.NumOfUnitsInFirstlayer; 
    opt.numHiddenUnits2 = opt.NumOfUnitsInSecondlayer;  
    opt.numHiddenUnits3 = opt.NumOfUnitsInThirdlayer; 
elseif opt.NumOfHiddenLayers ==4
    opt.numHiddenUnits1 = opt.NumOfUnitsInFirstlayer; 
    opt.numHiddenUnits2 = opt.NumOfUnitsInSecondlayer;  
    opt.numHiddenUnits3 = opt.NumOfUnitsInThirdlayer; 
    opt.numHiddenUnits4 = opt.NumOfUnitsInFourthlayer; 
end
% MLP parameters
if opt.NumOfFeedForwardLeyars ==1
    opt.ShallowhiddenLayerSize = [opt.NumOfNeuronsInFirstlayer];                                       % number of Hidden layers in MLP network.
elseif opt.NumOfFeedForwardLeyars ==2
    opt.ShallowhiddenLayerSize = [opt.NumOfNeuronsInFirstlayer opt.NumOfNeuronsInSecondlayer];          % number of Hidden layers in MLP network. 
elseif opt.NumOfFeedForwardLeyars ==3
    opt.ShallowhiddenLayerSize = [opt.NumOfNeuronsInFirstlayer opt.NumOfNeuronsInSecondlayer opt.NumOfNeuronsInThirdlayer];% number of Hidden layers in MLP network.
end



% prepare delays for time serie network
data = CreateTimeSeriesData(opt,data);

% divide data into test and train data
data = dataPartitioning(opt,data);

if strcmpi(opt.learningMethod,'LSTM')
    % LSTM data form
    data = LSTMInput(data);
    %  Define LSTM  architect 
    opt = LSTMArchitect(opt,data);
elseif strcmpi(opt.learningMethod,'MLP')
    % Prepare input data for MLP network.
    FeedForwardInput();
    %  Define MLP architect 
    opt  = FeedForwardArchitect(opt);    
end

% Train LSTM, MLP 
data = TrainNet(opt,data);

end
% make some delays on input filed
function data = CreateTimeSeriesData(opt,data)
    Delays = opt.Delays;

    x = data.x';
    T = size(x,2);
    
    MaxDelay = max(Delays);
    
    Range = MaxDelay+1:T;
    
    X= [];
    for d = Delays
        X=[X; x(:,Range-d)];
    end
    
    Y = x(:,Range);
    data.X  = X;
    data.Y  = Y;
end
% partitioning input data 
function data = dataPartitioning(opt,data)
data.XTr   = [];
data.YTr   = [];
data.XTs   = [];
data.YTs   = [];

numTrSample = round(opt.trPercentage*size(data.X,2));

data.XTr   = data.X(:,1:numTrSample);
data.YTr   = data.Y(:,1:numTrSample);

data.XTs   = data.X(:,numTrSample+1:end);
data.YTs   = data.Y(:,numTrSample+1:end);

disp(['Time Series data divided to ' num2str(opt.trPercentage*100) '% Train data and ' num2str((1-opt.trPercentage)*100) '% Test data']);
end
% Prepare input data for MLP network.
function FeedForwardInput()
    disp('Time Series data prepared as suitable MLP Input data.');
end
% Prepare input data for LSTM network.
function data = LSTMInput(data)

for i=1:size(data.XTr,2)
    XTr{i,1} = data.XTr(:,i);  
    YTr(i,1) = data.YTr(:,i);  
end

for i=1:size(data.XTs,2)
    XTs{i,1} =  data.XTs(:,i);  
    YTs(i,1) =  data.YTs(:,i); 
end
data.XTr   = XTr;
data.YTr   = YTr;
data.XTs   = XTs;
data.YTs   = YTs;

disp('Time Series data prepared as suitable LSTM Input data.');
end

% ---- network structure ----
% bi-LSTM Deeplearning Architect
function opt = LSTMArchitect(opt,data)

miniBatchSize   = opt.miniBatchSize;
maxEpochs       = opt.maxEpochs;
trainingProgress = opt.trainingProgress;
executionEnvironment = opt.executionEnvironment;

inputSize = size(data.X,1);
outputMode = 'last';
numResponses   = 1;
dropoutVal = opt.DropoutValue;
if opt.isUseDropoutLayer % if dropout layer is true
    if opt.NumOfHiddenLayers ==1
        if opt.isUseBiLSTMLayer == 1
            opt.layers = [ ...
                sequenceInputLayer(inputSize)
                bilstmLayer(opt.numHiddenUnits1,'OutputMode',outputMode)
                dropoutLayer(dropoutVal)
                fullyConnectedLayer(numResponses)
                regressionLayer];
        else
            opt.layers = [ ...
                sequenceInputLayer(inputSize)
                lstmLayer(opt.numHiddenUnits1,'OutputMode',outputMode)
                dropoutLayer(dropoutVal)
                fullyConnectedLayer(numResponses)
                regressionLayer];
        end
    elseif opt.NumOfHiddenLayers ==2
        if opt.isUseBiLSTMLayer
            opt.layers = [ ...
            sequenceInputLayer(inputSize)
            bilstmLayer(opt.numHiddenUnits1,'OutputMode','sequence')
            dropoutLayer(dropoutVal)
            bilstmLayer(opt.numHiddenUnits2,'OutputMode',outputMode)
            dropoutLayer(dropoutVal)
            fullyConnectedLayer(numResponses)
            regressionLayer];
        else
            opt.layers = [ ...
            sequenceInputLayer(inputSize)
            lstmLayer(opt.numHiddenUnits1,'OutputMode','sequence')
            dropoutLayer(dropoutVal)
            lstmLayer(opt.numHiddenUnits2,'OutputMode',outputMode)
            dropoutLayer(dropoutVal)
            fullyConnectedLayer(numResponses)
            regressionLayer];
        end
    elseif opt.NumOfHiddenLayers ==3
        if opt.isUseBiLSTMLayer
            opt.layers = [ ...
            sequenceInputLayer(inputSize)
            bilstmLayer(opt.numHiddenUnits1,'OutputMode','sequence')
            dropoutLayer(dropoutVal)
            bilstmLayer(opt.numHiddenUnits2,'OutputMode','sequence')
            dropoutLayer(dropoutVal)
            bilstmLayer(opt.numHiddenUnits3,'OutputMode',outputMode)
            dropoutLayer(dropoutVal)
            fullyConnectedLayer(numResponses)
            regressionLayer];
        else
            opt.layers = [ ...
            sequenceInputLayer(inputSize)
            bilstmLayer(opt.numHiddenUnits1,'OutputMode','sequence')
            dropoutLayer(dropoutVal)
            bilstmLayer(opt.numHiddenUnits2,'OutputMode','sequence')
            dropoutLayer(dropoutVal)
            bilstmLayer(opt.numHiddenUnits3,'OutputMode',outputMode)
            dropoutLayer(dropoutVal)
            fullyConnectedLayer(numResponses)
            regressionLayer];
        end
    elseif opt.NumOfHiddenLayers ==4
        if opt.isUseBiLSTMLayer
            opt.layers = [ ...
            sequenceInputLayer(inputSize)
            bilstmLayer(opt.numHiddenUnits1,'OutputMode','sequence')
            dropoutLayer(dropoutVal)
            bilstmLayer(opt.numHiddenUnits2,'OutputMode','sequence')
            dropoutLayer(dropoutVal)
            bilstmLayer(opt.numHiddenUnits3,'OutputMode','sequence')
            dropoutLayer(dropoutVal)
            bilstmLayer(opt.numHiddenUnits4,'OutputMode',outputMode)
            dropoutLayer(dropoutVal)
            fullyConnectedLayer(numResponses)
            regressionLayer];
        else
            opt.layers = [ ...
            sequenceInputLayer(inputSize)
            bilstmLayer(opt.numHiddenUnits1,'OutputMode','sequence')
            dropoutLayer(dropoutVal)
            bilstmLayer(opt.numHiddenUnits2,'OutputMode','sequence')
            dropoutLayer(dropoutVal)
            bilstmLayer(opt.numHiddenUnits3,'OutputMode','sequence')
            dropoutLayer(dropoutVal)
            bilstmLayer(opt.numHiddenUnits4,'OutputMode',outputMode)
            dropoutLayer(dropoutVal)
            fullyConnectedLayer(numResponses)
            regressionLayer];
        end
    end
else % if dropout layer is false
    if opt.NumOfHiddenLayers ==1
        if opt.isUseBiLSTMLayer
            opt.layers = [ ...
                sequenceInputLayer(inputSize)
                bilstmLayer(opt.numHiddenUnits1,'OutputMode',outputMode)
                fullyConnectedLayer(numResponses)
                regressionLayer];
        else
            opt.layers = [ ...
                sequenceInputLayer(inputSize)
                lstmLayer(opt.numHiddenUnits1,'OutputMode',outputMode)
                fullyConnectedLayer(numResponses)
                regressionLayer];
        end
    elseif opt.NumOfHiddenLayers ==2
        if opt.isUseBiLSTMLayer
            opt.layers = [ ...
            sequenceInputLayer(inputSize)
            bilstmLayer(opt.numHiddenUnits1,'OutputMode','sequence')
            bilstmLayer(opt.numHiddenUnits2,'OutputMode',outputMode)
            fullyConnectedLayer(numResponses)
            regressionLayer];
        else
            opt.layers = [ ...
            sequenceInputLayer(inputSize)
            lstmLayer(opt.numHiddenUnits1,'OutputMode','sequence')
            lstmLayer(opt.numHiddenUnits2,'OutputMode',outputMode)
            fullyConnectedLayer(numResponses)
            regressionLayer];
        end
    elseif opt.NumOfHiddenLayers ==3
        if opt.isUseBiLSTMLayer
            opt.layers = [ ...
            sequenceInputLayer(inputSize)
            bilstmLayer(opt.numHiddenUnits1,'OutputMode','sequence')
            bilstmLayer(opt.numHiddenUnits2,'OutputMode','sequence')
            bilstmLayer(opt.numHiddenUnits3,'OutputMode',outputMode)
            fullyConnectedLayer(numResponses)
            regressionLayer];
        else
            opt.layers = [ ...
            sequenceInputLayer(inputSize)
            bilstmLayer(opt.numHiddenUnits1,'OutputMode','sequence')
            bilstmLayer(opt.numHiddenUnits2,'OutputMode','sequence')
            bilstmLayer(opt.numHiddenUnits3,'OutputMode',outputMode)
            fullyConnectedLayer(numResponses)
            regressionLayer];
        end
    elseif opt.NumOfHiddenLayers ==4
        if opt.isUseBiLSTMLayer
            opt.layers = [ ...
            sequenceInputLayer(inputSize)
            bilstmLayer(opt.numHiddenUnits1,'OutputMode','sequence')
            bilstmLayer(opt.numHiddenUnits2,'OutputMode','sequence')
            bilstmLayer(opt.numHiddenUnits3,'OutputMode','sequence')
            bilstmLayer(opt.numHiddenUnits4,'OutputMode',outputMode)
            fullyConnectedLayer(numResponses)
            regressionLayer];
        else
            opt.layers = [ ...
            sequenceInputLayer(inputSize)
            bilstmLayer(opt.numHiddenUnits1,'OutputMode','sequence')
            bilstmLayer(opt.numHiddenUnits2,'OutputMode','sequence')
            bilstmLayer(opt.numHiddenUnits3,'OutputMode','sequence')
            bilstmLayer(opt.numHiddenUnits4,'OutputMode',outputMode)
            fullyConnectedLayer(numResponses)
            regressionLayer];
        end
    end
end
% Training Network Options
% 'sgdm' 
% 'rmsprop'
% 'adam'

opt.opts = trainingOptions(opt.LR, ...
    'MaxEpochs',maxEpochs, ...
    'GradientThreshold',1, ...
    'InitialLearnRate',0.005, ...
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropPeriod',125, ...
    'LearnRateDropFactor',0.2, ...
    'Verbose',1, ...
    'MiniBatchSize',miniBatchSize,...
    'ExecutionEnvironment',executionEnvironment,...
    'Plots',trainingProgress);
    disp('LSTM architect successfully created.');
end
% MLP Shallowlearning Architect
function opt = FeedForwardArchitect(opt)
opt.Net = feedforwardnet(opt.ShallowhiddenLayerSize,opt.trainFcn);
opt.Net.divideParam.trainRatio = 80/100;
opt.Net.divideParam.valRatio  = 10/100;
opt.Net.divideParam.testRatio = 10/100;

opt.Net.trainParam.epochs          = opt.maxItrations;
opt.Net.trainParam.showWindow      = opt.showWindow;
opt.Net.trainParam.showCommandLine = opt.showCommandLine;
disp('MLP architect successfully created.');

end
% Train  Network
function data = TrainNet(opt,data)

if strcmpi(opt.learningMethod,'LSTM')
    try
        data.BiLSTM.Net = trainNetwork(data.XTr,data.YTr,opt.layers,opt.opts);
        disp('LSTM Netwwork successfully trained.');
        data.IsNetTrainSuccess =true;
    catch me
        disp('Error on Training LSTM Network');
        data.IsNetTrainSuccess = false;
        return;
    end
elseif strcmpi(opt.learningMethod,'MLP')
    try
        [data.FF.Net,~] = train(opt.Net,data.XTr,data.YTr);
        disp('Feed Forward Netwwork successfully trained.');
        data.IsNetTrainSuccess = true;
    catch me
        disp('Error on Training FF Network');
        data.IsNetTrainSuccess =false;
        return;
    end
end

end


% --------------- Evaluate Data ---------------
% ---------------------------------------------
function [opt,data] = EvaluationData(opt,data)
if strcmpi(opt.learningMethod,'LSTM')
    
    data.BiLSTM.TrainOutputs = deNorm(data.seriesdata,predict(data.BiLSTM.Net,data.XTr,'MiniBatchSize',opt.miniBatchSize),opt.dataPreprocessMode);
    data.BiLSTM.TrainTargets = deNorm(data.seriesdata,data.YTr,opt.dataPreprocessMode);
    data.BiLSTM.TestOutputs  = deNorm(data.seriesdata,predict(data.BiLSTM.Net,data.XTs,'MiniBatchSize',opt.miniBatchSize),opt.dataPreprocessMode);
    data.BiLSTM.TestTargets  = deNorm(data.seriesdata,data.YTs,opt.dataPreprocessMode);
    data.BiLSTM.AllDataTargets = [data.BiLSTM.TrainTargets data.BiLSTM.TestTargets];
    data.BiLSTM.AllDataOutputs = [data.BiLSTM.TrainOutputs data.BiLSTM.TestOutputs];
   
    data = PlotResults(data,'Tr',...
        data.BiLSTM.TrainOutputs, ...
        data.BiLSTM.TrainTargets);
    data = plotReg(data,'Tr',data.BiLSTM.TrainTargets,data.BiLSTM.TrainOutputs);

    data = PlotResults(data,'Ts',....
        data.BiLSTM.TestOutputs, ...
        data.BiLSTM.TestTargets);
    data = plotReg(data,'Ts',data.BiLSTM.TestTargets,data.BiLSTM.TestOutputs);

    data = PlotResults(data,'All',...
        data.BiLSTM.AllDataOutputs, ...
        data.BiLSTM.AllDataTargets);
    data = plotReg(data,'All',data.BiLSTM.AllDataTargets,data.BiLSTM.AllDataOutputs);
    
    disp('Bi-LSTM network performance evaluated.');
    
elseif strcmpi(opt.learningMethod,'MLP')
    
        data.FF.TrainOutputs = deNorm(data.seriesdata,data.FF.Net(data.XTr)',opt.dataPreprocessMode);
        data.FF.TrainTargets = deNorm(data.seriesdata,(data.YTr)',opt.dataPreprocessMode);
        data.FF.TestOutputs  = deNorm(data.seriesdata,data.FF.Net(data.XTs)',opt.dataPreprocessMode);
        data.FF.TestTargets  = deNorm(data.seriesdata,(data.YTs)',opt.dataPreprocessMode);

    data.FF.AllDataTargets = [data.FF.TrainTargets data.FF.TestTargets];
    data.FF.AllDataOutputs = [data.FF.TrainOutputs data.FF.TestOutputs];
 
    DispVal = 1;
    for i= DispVal
        data = PlotResults(data,'Tr',...
            data.FF.TrainOutputs(i,:), ...
            data.FF.TrainTargets(i,:));
        data = plotReg(data,'Tr',data.FF.TrainTargets(i,:),data.FF.TrainOutputs(i,:));
        data = PlotResults(data,'Ts',....
            data.FF.TestOutputs(i,:), ...
            data.FF.TestTargets(i,:));
        data = plotReg(data,'Ts',data.FF.TestTargets(i,:),data.FF.TestOutputs(i,:));
        data = PlotResults(data,'All',...
            data.FF.AllDataOutputs(i,:), ...
            data.FF.AllDataTargets(i,:));
        data = plotReg(data,'All',data.FF.AllDataTargets(i,:),data.FF.AllDataOutputs(i,:));
        disp('MLP network performance evaluated.');
    end

end
end
function vars = deNorm(data,stdData,deNormMode)
if iscell(stdData(1,1))
        for i=1:size(stdData,1)
            tmp(i,:) = stdData{i,1}';
        end
        stdData = tmp;
end
if strcmpi(deNormMode,'Data Normalization')
    for i=1:size(data,2)
        vars(:,i) = (stdData(:,i).*(max(data(:,i))-min(data(:,i)))) + min(data(:,i));
    end
    vars = vars';
    
elseif strcmpi(deNormMode,'Data Standardization')
    for i=1:size(data,2)
        x.mu(1,i)   = mean(data(:,i),'omitnan');
        x.sig(1,i)  = std (data(:,i),'omitnan');
        vars(:,i) = ((stdData(:,i).* x.sig(1,i))+ x.mu(1,i));
    end
    vars = vars';
    
else
    vars = stdData';
    return;
end
end
% plot the output of networks and real output on test and train data
function data = PlotResults(data,firstTitle,Outputs,Targets)  
Errors = Targets - Outputs;
    MSE   = mean(Errors.^2);
    RMSE  = sqrt(MSE);
    NRMSE = RMSE/mean(Targets);
    ErrorMean = mean(Errors);
    ErrorStd  = std(Errors);
    rankCorre = RankCorre(Targets,Outputs);

    if strcmpi(firstTitle,'tr')
         Disp1Name = 'OutputGraphEvaluation_TrainData';
         Disp2Name = 'ErrorEvaluation_TrainData';
         Disp3Name = 'ErrorHistogram_TrainData';
    elseif strcmpi(firstTitle,'ts')
        Disp1Name = 'OutputGraphEvaluation_TestData';
        Disp2Name = 'ErrorEvaluation_TestData';
        Disp3Name = 'ErrorHistogram_TestData';
    elseif strcmpi(firstTitle,'all')
        Disp1Name = 'OutputGraphEvaluation_ALLData';
        Disp2Name = 'ErrorEvaluation_ALLData';
        Disp3Name = 'ErrorHistogram_AllData';
    end
    
    figure('Name',Disp1Name,'NumberTitle','off');
    plot(1:length(Targets),Targets,...
        1:length(Outputs),Outputs);grid minor
    legend('Targets','Outputs','Location','best') ;
    title(['Rank Correlation = ' num2str(rankCorre)]);
   
    figure('Name',Disp2Name,'NumberTitle','off');
    plot(Errors);grid minor
    title({['MSE = ' num2str(MSE) ', RMSE = ' num2str(RMSE)...
        ' NRMSE = ' num2str(NRMSE)] ;});
    xlabel(['Error Per Sample']);
 
    figure('Name',Disp3Name,'NumberTitle','off');
    histogram(Errors);grid minor

    title(['Error Mean = ' num2str(ErrorMean) ', Error StD = ' num2str(ErrorStd)]);
    xlabel(['Error Histogram']);
    
    if strcmpi(firstTitle,'tr')
        data.Err.MSETr = MSE;
        data.Err.STDTr = ErrorStd;
        data.Err.NRMSETr     = NRMSE;
        data.Err.rankCorreTr = rankCorre;
    elseif strcmpi(firstTitle,'ts')
        data.Err.MSETs = MSE;
        data.Err.STDTs = ErrorStd;
        data.Err.NRMSETs     = NRMSE;
        data.Err.rankCorreTs = rankCorre;
    elseif strcmpi(firstTitle,'all')
        data.Err.MSEAll = MSE;
        data.Err.STDAll = ErrorStd;
        data.Err.NRMSEAll     = NRMSE;
        data.Err.rankCorreAll = rankCorre;
    end
end
% find rank correlation between network output and real data
function [r]=RankCorre(x,y)
x=x';
y=y';
% Find the data length
N = length(x);
% Get the ranks of x
R = crank(x)';
for i=1:size(y,2)
    % Get the ranks of y
    S = crank(y(:,i))';
    % Calculate the correlation coefficient
    r(i) = 1-6*sum((R-S).^2)/N/(N^2-1); %#ok
end
end
function r=crank(x)
u = unique(x);
[~,z1] = sort(x);
[~,z2] = sort(z1);
r = (1:length(x))';
r=r(z2);
for i=1:length(u)
    s=find(u(i)==x);
    r(s,1) = mean(r(s));
end
end
% plot the regression line of output and real value
function data = plotReg(data,Title,Targets,Outputs)

 if strcmpi(Title,'tr')
     DispName = 'RegressionGraphEvaluation_TrainData';
elseif strcmpi(Title,'ts')
    DispName = 'RegressionGraphEvaluation_TestData';
elseif strcmpi(Title,'all')
    DispName = 'RegressionGraphEvaluation_ALLData';
end
figure('Name',DispName,'NumberTitle','off');
x = Targets';
y = Outputs';
format long
b1 = x\y;
yCalc1 = b1*x;
scatter(x,y,'MarkerEdgeColor',[0 0.4470 0.7410],'LineWidth',.7);
hold('on');
plot(x,yCalc1,'Color',[0.8500 0.3250 0.0980]);
xlabel('Prediction');
ylabel('Target');
grid minor
% xgrid = 'on';
% disp.YGrid = 'on';
X = [ones(length(x),1) x];
b = X\y;
yCalc2 = X*b;
plot(x,yCalc2,'-.','MarkerSize',4,"LineWidth",.1,'Color',[0.9290 0.6940 0.1250])
legend('Data','Fit','Y=T','Location','best');
%
Rsq2 = 1 -  sum((y - yCalc1).^2)/sum((y - mean(y)).^2);

if strcmpi(Title,'tr')
    data.Err.RSqur_Tr = Rsq2;
    title(['Train Data, R^2 = ' num2str(Rsq2)]);
elseif strcmpi(Title,'ts')
    data.Err.RSqur_Ts = Rsq2;
    title(['Test Data, R^2 = ' num2str(Rsq2)]);
elseif strcmpi(Title,'all')
    data.Err.RSqur_All = Rsq2;
    title(['All Data, R^2 = ' num2str(Rsq2)]);
end

end

[Giuseppe D'Amico]

The error is this:

Input data successfully read.

Time Series data divided to 80% Train data and 20% Test data

Time Series data prepared as suitable LSTM Input data.

LSTM architect successfully created.

Error on Training LSTM Network

Reference to non-existent field 'BiLSTM'.

Error in TimeSeriesPrediction>EvaluationData (line 477)

data.BiLSTM.TrainOutputs =

deNorm(data.seriesdata,predict(data.BiLSTM.Net,data.XTr,'MiniBatchSize',opt.miniBatchSize),opt.dataPreprocessMode);

Error in TimeSeriesPrediction (line 50)

[opt,data] = EvaluationData(opt,data);

[Carlos Matas Acosta]

I jumped into the same issue, simply run the subsections of the top level code individually. This generates the data.BiLSTM sub-struct after training is completed, so you can proceed to the results evaluation function. It somehow has stopped happening to me after running the code twice.

[NN]

Can someone help me to understand this portion in the code

opt.isUseBiLSTMLayer = true; % if it is true the layer turn to the Bidirectional-LSTM and if it is false it will turn the units to the simple LSTM

opt.isUseDropoutLayer = true;

How thsi command works?

[Yusuf Ali Danis]

Above the code (line 6) you can change learning method as "MLP" rather than "LSTM". In this case, at least you will not get any error messages and run with MLP.

Answer 1

[dpb]

NB the following ouput:

Input data successfully read.
Time Series data divided to 80% Train data and 20% Test data
Time Series data prepared as suitable LSTM Input data.
LSTM architect successfully created.
Error on Training LSTM Network
...

The model training failed with your data set, so you can't reasonably expect to be able to evaluate a non-existing model.

NB2: The original code has a return from the "TrainNet" function, but there's no error checking that tests for success between the top level code that calls

...
%% --------------- load Data
data = loadData(opt);
if ~data.isDataRead
    return;
end
%% --------------- Train Network
[opt,data] = TrainData(opt,data);
%% --------------- Evaluate Data 
[opt,data] = EvaluationData(opt,data);

%% ---------------------------- Local Functions ---------------------------
function data = loadData(opt)
  ...

TrainData and the next line EvaluationData so the code just keeps on going. Obviously, the original writer of the code must have had some additional structure in the actual application to prevent such an event (or his/her data was such training never failed so never ran into the issue).

As a start, you can

  if strcmpi(opt.learningMethod,'LSTM')
    try
        data.BiLSTM.Net = trainNetwork(data.XTr,data.YTr,opt.layers,opt.opts);
        disp('LSTM Netwwork successfully trained.');
        data.IsNetTrainSuccess =true;
    catch me
        disp('Error on Training LSTM Network');
        data.IsNetTrainSuccess = false;
        return;
    end

look at the above code and see that an error flag is set in the data struct so after

%% --------------- Train Network
[opt,data] = TrainData(opt,data);
assert(data.IsNetTrainSuccess,'Net Train Failed. Aborting')

will at least keep you from running off the end.

We, of course, can do nothing regarding why the training failed.

There should also be a return statement after the next line to prevent the code from just "falling through" from the high level code to the local functions.

[Yusuf Ali Danis]

Above the code (line 6) you can change learning method as "MLP" rather than "LSTM". In this case you will not get any error messages and run with MLP.