We extend the Bayesian framework to Multi-Layer Perceptron models of Non-linear Auto-Regressive time-series. The approach is evaluated on an artificial time-series and some common simplifications are discussed.
Mémoire: Géométrie algébrique et géométrie differentielle projective. Areas of research: Applications of representation theory and geometry to complexity theory, exterior differential systems, projective algebraic geometry, homogeneous varieties, geometry of tensors and applications.
Recurrent neural networks possess interesting universal approximation capabilities, making them good candidates for time series modeling. Unfortunately, long term dependencies are difficult to learn if gradient descent algorithms are employed. We support the view that it is easier for these algorithms to find good solutions if one includes connections with… (More)
sets of neural networks and we use the mean predictions. Also, we develop models on different training sets and test them on different tests sets before concluding that one class of models is superior to the others. We study the problem of generating monthly forecasts of the CAC 40 financial index. We compare the results obtained by linear models and… (More)