Given n ordered sets, each containing 6 numbers, generate the next set in the sequence I am facing the following problem in machine learning. Given n ordered sets, each containing 6 numbers, generate the next set in the sequence. The numbers in a set are not random.
For instance, it could be that these numbers are generated by an equation, e.g. $(1+x^y)$ so that the form the following sequence:

[{1, 2, 3, 4, 5, 6}, {1, 2, 5, 10, 17, 26}, {1, 2, 9, 28, 65, 126}, ..., {1, 2, 1 + 2**n, 1 + 3**n, 1 + 4**n, 1 + 5**n}]


But this equation or any other rule governing the order is not known. We are given a large n of these sets. What would be the most appropriate way to predict this in Keras?
This should just be a sequence prediction problem, and a multivariate one. You can use LSTMs or GRUs in Keras to handle the sequences. In the simplest architecture, the inputs will be batches of (it seems) n-1 steps of 6 numbers, and the output will be the final 6 numbers in the sequence. This is pretty much how any RNN works. The loss will be mean_squared_error, I presume.

But given how much is unknown here about the distribution of the sequences, it's unclear whether this simple approach would work well, or whether even a more complex network can learn whatever patterns these follow.