Why my validation RMSE and loss increase after some epoch by my training data increase

Hello everyone
I am trying to predict traffic flow of future steps by previous collected data so I Use LSTM for it
but my validation loss and rmse increase and training loss and rmse decrease .because I am net to LSTM I don’t know which parameters I should check for improving model and predictions.
the picture of training progress is :

also I use different lags time for my predictions and here in my codes I have 4 step lag time
XTrain_ZaMir = (XTrain_ZaMir – mu_ZaMir)/sig_ZaMir;
YTrain_ZaMir = (YTrain_ZaMir – mu_ZaMir)/sig_ZaMir;
XTrain_ZaMir = XTrain_ZaMir(:,1:end-4);
YTrain_ZaMir = YTrain_ZaMir(:,5:end);
Test_ZaMir = [flowTe_ZaMir flowTeOther_ZaMir]’;
nt = floor(0.7*length(Test_ZaMir));
YTest_ZaMir = Test_ZaMir(1,1:end);
XTest_ZaMir = Test_ZaMir(1,1:end); %One input

% XTest_ZaMir = Test_ZaMir(:,1:end); % More than One input
XTest_ZaMir = (XTest_ZaMir – mu_ZaMir)/sig_ZaMir;
YTest_ZaMir = (YTest_ZaMir – mu_ZaMir)/sig_ZaMir;
XVal_ZaMir = XTest_ZaMir(:,1:nt-4);
YVal_ZaMir = YTest_ZaMir(:,5:nt);
XTest_ZaMir = XTest_ZaMir(:,nt+4:end-1);
YTest_ZaMir = YTest_ZaMir(:,nt+5:end);

%% Layers and Options
numResponses = 1 ;
featureDimension = 1;
numHiddenUnits =200 ;

layers = [ …
sequenceInputLayer(featureDimension)
lstmLayer(numHiddenUnits)
% dropoutLayer(0.002)
fullyConnectedLayer(numResponses)
regressionLayer
];

maxepochs = 250;
minibatchsize =128;

options = trainingOptions(‘adam’, … %%adam
‘MaxEpochs’,maxepochs, …
‘GradientThreshold’,1, …
‘InitialLearnRate’,0.005, …
‘ValidationData’,{XVal_ZaMir,YVal_ZaMir},…
‘ValidationFrequency’,20,…
‘Shuffle’,’every-epoch’,…
‘MiniBatchSize’,minibatchsize,…
‘LearnRateSchedule’,’piecewise’, …
‘LearnRateDropPeriod’,150, …
‘LearnRateDropFactor’,0.005, …
‘Verbose’,1, …
‘Plots’,’training-progress’);

%% Train the Network
[net,info] = trainNetwork(XTrain_ZaMir,YTrain_ZaMir,layers,options);
[net,YPred_ZaMir]= predictAndUpdateState(net,XTest_ZaMir);

numTimeStepsTest= (0.5*floor(length(XTest_ZaMir)));
for i = 2:numTimeStepsTest
[net,YPred_ZaMir(:,i)] = predictAndUpdateState(net,XTest_ZaMir(:,i-1),’ExecutionEnvironment’,’cpu’);
% net = resetState(net);
end
YTest_ZaMir = sig_ZaMir*YTest_ZaMir + mu_ZaMir;
YPred_ZaMir = sig_ZaMir*YPred_ZaMir + mu_ZaMir;Hello everyone
I am trying to predict traffic flow of future steps by previous collected data so I Use LSTM for it
but my validation loss and rmse increase and training loss and rmse decrease .because I am net to LSTM I don’t know which parameters I should check for improving model and predictions.
the picture of training progress is :

%% Layers and Options
numResponses = 1 ;
featureDimension = 1;
numHiddenUnits =200 ;

layers = [ …
sequenceInputLayer(featureDimension)
lstmLayer(numHiddenUnits)
% dropoutLayer(0.002)
fullyConnectedLayer(numResponses)
regressionLayer
];

maxepochs = 250;
minibatchsize =128;

%% Train the Network
[net,info] = trainNetwork(XTrain_ZaMir,YTrain_ZaMir,layers,options);
[net,YPred_ZaMir]= predictAndUpdateState(net,XTest_ZaMir);

numTimeStepsTest= (0.5*floor(length(XTest_ZaMir)));
for i = 2:numTimeStepsTest
[net,YPred_ZaMir(:,i)] = predictAndUpdateState(net,XTest_ZaMir(:,i-1),’ExecutionEnvironment’,’cpu’);
% net = resetState(net);
end
YTest_ZaMir = sig_ZaMir*YTest_ZaMir + mu_ZaMir;
YPred_ZaMir = sig_ZaMir*YPred_ZaMir + mu_ZaMir; Hello everyone
I am trying to predict traffic flow of future steps by previous collected data so I Use LSTM for it
but my validation loss and rmse increase and training loss and rmse decrease .because I am net to LSTM I don’t know which parameters I should check for improving model and predictions.
the picture of training progress is :

%% Layers and Options
numResponses = 1 ;
featureDimension = 1;
numHiddenUnits =200 ;

layers = [ …
sequenceInputLayer(featureDimension)
lstmLayer(numHiddenUnits)
% dropoutLayer(0.002)
fullyConnectedLayer(numResponses)
regressionLayer
];

maxepochs = 250;
minibatchsize =128;

%% Train the Network
[net,info] = trainNetwork(XTrain_ZaMir,YTrain_ZaMir,layers,options);
[net,YPred_ZaMir]= predictAndUpdateState(net,XTest_ZaMir);

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