time series. This implies that MLPs need less coefficients than the RBFs to return the

same accuracy, but it also means that it is a lot more difficult for an MLP to track change

(one slight evolution in the time series could require that all the weights of the network be

retrained, whereas only one RBF center would be moved). Besides, we only adapted the

output layer online, which makes it even more difficult for the MLP to adapt. This could

explain why the MLP does not achieve such better prediction (i.e., smaller criterion) than

the RBF although they have the same number of weights, and the MLP would be

expected to perform a lot better.

 Interestingly, the three types of experts perform good segmentation, each with

some particularities (each RBF seems to predict better one of the regimes for example):

the importance in the choice of the type of model for segmentation is emphasized, and for

this data the RBF network is the most satisfying one.

5.2.2 Practical Considerations

 Non-linear dynamics modeling is still part art part science, because there is no

universal technique that works for all data. The key in designing all these systems,

indeed, is to find the best set of parameters for the application at stake: the number of

experts, size of hidden layer of those experts and embedding size and lag, the memory

depth of the criterion, but also the learning rates for the adaptation algorithms, the

competition parameter of the gate and most of all the initial values of the networks. All

those parameters have to be set optimally by hand, and external knowledge about the data

is extremely useful in doing so. One important missing piece of knowledge is the actual

switching history that would allow us to numerically assess the accuracy of segmentation

in terms of delay to detect a change, and length of each detected regime. There is no