I believe the default LSTM has outputs bounded in (-1,1) due to the activation functions used.
In any case you can try using activations to add constraints to your model, e.g. a tanhLayer has outputs bounded in (-1,1) and you can add a fixed linear transform to modify that to an arbitrary interval (a,b) as follows:
a = 0; b = 10;
% since tanh(x) is in (-1,1), (tanh(x) + 1)/2 is in (0,1).
% then scale to (0,b-a) and translate to (a,b);
boundToIntervalLayer = functionLayer(@(x) a + (b-a)*(tanh(x)+1)/2);
layers = [
sequenceInputLayer(1)
lstmLayer(1)
fullyConnectedLayer(1)
boundToIntervalLayer];
net = dlnetwork(layers);
% test it out
seqLen = 10;
x = dlarray(randn(1,seqLen),"CT");
y = forward(net,x);
% you can check y is in (a,b)
Alternatively if you use a custom training loop you could add a "soft constraint" by adding a penalty term to the loss function that is large when the network predicts values outside of your bounds.
