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;