Can anyone give me a practicale example of a recurrent neural network in (pybrain) python in order to predict the next value of a sequence ? (I've read the pybrain documentation and there is no clear example for it I think.) I also found this question. But I fail to see how it works in a more general case. So therefore I'm asking if anyone here could work out a clear example of how to predict the next value of a sequence in pybrain, with a recurrent neural network.

To give an example.

Say for example we have a sequence of numbers in the range [1,7].

First run (So first example): 1 2 4 6 2 3 4 5 1 3 5 6 7 1 4 7 1 2 3 5 6 Second run (So second example): 1 2 5 6 2 4 4 5 1 2 5 6 7 1 4 6 1 2 3 3 6 Third run (So third example): 1 3 5 7 2 4 6 7 1 3 5 6 7 1 4 6 1 2 2 3 7 and so on.

Now given for example the start of a new sequence: 1 3 5 7 2 4 6 7 1 3

what is/are the next value(s)

This question might seem lazy, but I think there lacks a good and decent example of how to do this with pybrain.

Additionally: How can this be done if more than 1 feature is present:

Example:

Say for example we have several sequences (each sequence having 2 features) in the range [1,7].

First run (So first example): feature1: 1 2 4 6 2 3 4 5 1 3 5 6 7 1 4 7 1 2 3 5 6 feature2: 1 3 5 7 2 4 6 7 1 3 5 6 7 1 4 6 1 2 2 3 7 Second run (So second example): feature1: 1 2 5 6 2 4 4 5 1 2 5 6 7 1 4 6 1 2 3 3 6 feature2: 1 2 3 7 2 3 4 6 2 3 5 6 7 2 4 7 1 3 3 5 6 Third run (So third example): feature1: 1 3 5 7 2 4 6 7 1 3 5 6 7 1 4 6 1 2 2 3 7 feature2: 1 2 4 6 2 3 4 5 1 3 5 6 7 1 4 7 1 2 3 5 6 and so on.

Now given for example the start of a new sequences:

feature 1: 1 3 5 7 2 4 6 7 1 3 feature 2: 1 2 3 7 2 3 4 6 2 4

what is/are the next value(s)

Feel free to use your own example as long it is similar to these examples and has some in depth explanation.