# Univariate time series multi step ahead prediction using multi-layer-perceptron (MLP)

I have a univariate time series data. I want to do a multi-step prediction. I came across this question which explains time series one step prediction. but I am interested in multi-step ahead prediction. For example, a typical univariate time series data looks like this:

    time  value
----  ------
t1      a1
t2      a2
..........
..........
t100    a100.


Suppose, I want 3 step ahead prediction. Can I frame my problem like:

   TrainX                 TrainY
[a1,a2,a3,a4,a5,a6]   -> [a7,a8,a9]
[a2,a3,a4,a5,a6,a7]   -> [a8,a9,a10]
[a3,a4,a5,a6,a7,a8]   -> [a9,a10,a11]
..................        ...........
..................        ...........


(I am using keras and tensorflow as the backend.)

The first layer has 50 neurons and expects 6 inputs; the hidden layer has 30 neurons; and the output layer has 3 neurons (i.e., outputs three time series values).

model = Sequential()

model.fit(TrainX, TrainY, epochs=300, batch_size=16)


Is this a valid model? Am I missing something?