# neural network number of input neuron

I want to predict using a multi layer perceptron. My data have 3 attributes x,y,z. My window size (input) is 3. That is, using 3 sets of values for x,y,z to predict next set of x,y,z. What should be my number of input neurons?

Should it be 6 input neurons for 3 sets of values of x,y,z?
or should it be 3 input neurons and each neuron has 3 inputs for x,y,z?

My understanding of the question is that you are using a basic MLP, feed-forward neural network, to work with data that is a 3-dimensional vector which you are calling $(x,y,z)$ - although note that most notation you read would call the whole observation $x$ which would be a vector $(x_1, x_2, x_3)$. You want to accept an input of 1st, 2nd, 3rd observations of this data (a window on some possibly longer series), and predict the 4th observation.
For the given architecture, the prediction is made immediately on the supplied input - a MLP has no "memory" for previous inputs - so you will need 3 x 3 = 9 inputs to represent each (x,y,z) in the series. For a MLP it doesn't really matter how you arrange this input - e.g. you could have $(x^{(1)}, y^{(1)}, z^{(1)},x^{(2)}, y^{(2)}, z^{(2)},x^{(3)}, y^{(3)}, z^{(3)})$ or $(x^{(1)}, x^{(2)}, x^{(3)},y^{(1)}, y^{(2)}, y^{(3)},z^{(1)}, z^{(2)}, z^{(3)})$ or any other arrangement as long as you are consistent for every training example and prediction.
You don't usually want or need to somehow combine the x, y and z values to create a pre-processed layer with 3 inputs, or create a special layer that combines $x^{(1)}, x^{(2)}, x^{(3)}$. You might do that if your understanding of how the sequence worked allowed you to come up with features that modelled something important. However, the more usual approach is to allow the neural network to figure out how the multiple inputs combine from the training data.