Hallo!
Hm. You can incorporate changes over time using a standard feedforward ANN by wrapping your time-ordered vectors over a given time period into a single input vector and increasing the number of inputs to the network accordingly. But of course this introduces latency and other problems (e.g. it could massively increase the number of training examples required).
Pd has the ann_td external, which provides a 'time delay' neural network which I believe incorporates time using a method similar to that described above.
Yes of course, one other possibility is to use time delayed neural networks ...
For example a hidden markov model or echo state network (= special kind of recurrent neural network) should work.
I'm intrigued! Presumably these approaches avoid the latency problem by maintaining the network's state? Are there other advantages -- easier to train?
Hm ... I did not think about latency ... but if you do not process the data in blocks there should not be a significant latency (also for the time delay NN) ?
However, the advantage of the echo state network is that training is linear and you cannot get in a suboptimal solution as with feedforward neural networks (where the error surface has multiple local minimas) - see for example http://www.scholarpedia.org/article/Echo_state_network for a short introduction. And it is recurrent - so in general more powerful ... from the link above: "On a number of benchmark tasks, ESNs have starkly outperformed all other methods of nonlinear dynamical modelling"
If you are interested, I implemented ESNs (with various extensions) in a C++ library with python bindings: http://aureservoir.sourceforge.net/, a PD external will hopefully follow in summer ...
LG Georg