error using trainNetwork Invalid training data. Responses must be a matrix of numeric responses, or a N-by-1 cell array of sequences, where N is the number of sequences

Hi
I have this following code and the data set is traffic flow that gathered every 15 seconds and I want to predict data flow but i faced an error "Invalid training data. Responses must be a matrix of numeric responses, or a N-by-1 cell array of sequences, where N is the number of sequences. The feature dimension of all sequences must be the same"and I can’t fix it can anyone help me with it

My code is :
clc
close all
clear all
warning off
[~,~,flow_data] = xlsread(‘two_days.xlsx’); % Here we have two days data
data_mat = cell2mat(flow_data(2:end,3:4));

YTrain = data_mat(:,:)’;
XTrain = data_mat(:,:)’;
%
% %
XTrain = num2cell(XTrain,1)’;
YTrain = num2cell(YTrain,1)’;

numResponses = 1 ;
% numResponses = size(YTrain{1},1);
featureDimension = size(XTrain{1},1);
numHiddenUnits = 100;

layers = [ …
sequenceInputLayer(featureDimension)
lstmLayer(numHiddenUnits)
% dropoutLayer(0.1) %%0.5
fullyConnectedLayer(numResponses)
regressionLayer];

maxepochs = 500;
miniBatchSize = 1;

options = trainingOptions(‘adam’, … %%adam
‘MaxEpochs’,maxepochs, …
‘GradientThreshold’,1, …
‘InitialLearnRate’,0.005, …
‘LearnRateSchedule’,’piecewise’, …
‘LearnRateDropPeriod’,125, …
‘LearnRateDropFactor’,0.2, …
‘Verbose’,1, …
‘Plots’,’training-progress’);

%%Train the Network
net = trainNetwork(XTrain,YTrain,layers,options);

% YTest = data_mat(0.85*length(data_mat):end,1)’;
% XTest = data_mat(0.85*length(data_mat):end,1)’;
%
% XTest = num2cell(XTest,1);
% YTest = num2cell(YTest,1);
%
% net = resetState(net);
% YPred = predict(net,XTest)
%
%
% y1 = (cell2mat(YPred(1:end, 1:end))); %have to transpose as plot plots columns
% plot(y1)
% hold on
% y2 = (cell2mat(YTest(1:end, 1:end))’);
% plot(y2)Hi
I have this following code and the data set is traffic flow that gathered every 15 seconds and I want to predict data flow but i faced an error "Invalid training data. Responses must be a matrix of numeric responses, or a N-by-1 cell array of sequences, where N is the number of sequences. The feature dimension of all sequences must be the same"and I can’t fix it can anyone help me with it

My code is :
clc
close all
clear all
warning off
[~,~,flow_data] = xlsread(‘two_days.xlsx’); % Here we have two days data
data_mat = cell2mat(flow_data(2:end,3:4));

YTrain = data_mat(:,:)’;
XTrain = data_mat(:,:)’;
%
% %
XTrain = num2cell(XTrain,1)’;
YTrain = num2cell(YTrain,1)’;

numResponses = 1 ;
% numResponses = size(YTrain{1},1);
featureDimension = size(XTrain{1},1);
numHiddenUnits = 100;

layers = [ …
sequenceInputLayer(featureDimension)
lstmLayer(numHiddenUnits)
% dropoutLayer(0.1) %%0.5
fullyConnectedLayer(numResponses)
regressionLayer];

maxepochs = 500;
miniBatchSize = 1;

%%Train the Network
net = trainNetwork(XTrain,YTrain,layers,options);

% YTest = data_mat(0.85*length(data_mat):end,1)’;
% XTest = data_mat(0.85*length(data_mat):end,1)’;
%
% XTest = num2cell(XTest,1);
% YTest = num2cell(YTest,1);
%
% net = resetState(net);
% YPred = predict(net,XTest)
%
%
% y1 = (cell2mat(YPred(1:end, 1:end))); %have to transpose as plot plots columns
% plot(y1)
% hold on
% y2 = (cell2mat(YTest(1:end, 1:end))’);
% plot(y2) Hi
I have this following code and the data set is traffic flow that gathered every 15 seconds and I want to predict data flow but i faced an error "Invalid training data. Responses must be a matrix of numeric responses, or a N-by-1 cell array of sequences, where N is the number of sequences. The feature dimension of all sequences must be the same"and I can’t fix it can anyone help me with it

My code is :
clc
close all
clear all
warning off
[~,~,flow_data] = xlsread(‘two_days.xlsx’); % Here we have two days data
data_mat = cell2mat(flow_data(2:end,3:4));

YTrain = data_mat(:,:)’;
XTrain = data_mat(:,:)’;
%
% %
XTrain = num2cell(XTrain,1)’;
YTrain = num2cell(YTrain,1)’;

numResponses = 1 ;
% numResponses = size(YTrain{1},1);
featureDimension = size(XTrain{1},1);
numHiddenUnits = 100;

layers = [ …
sequenceInputLayer(featureDimension)
lstmLayer(numHiddenUnits)
% dropoutLayer(0.1) %%0.5
fullyConnectedLayer(numResponses)
regressionLayer];

maxepochs = 500;
miniBatchSize = 1;

%%Train the Network
net = trainNetwork(XTrain,YTrain,layers,options);

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