LAG space estimation in time series modelling

The purpose of this contribution is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the regressor vector a.k.a. input layer in a neural network. We give a rough description of the problem, insist on the concept of generalisation, and propose a generalisation-based method. We compare it to a non-parametric test, and carry out experiments, both on the well-known Henon map, and on a real data set.