Analysis of time series by neural networks

Neural networks have been constructed to analyze artificial time series derived from the Tent and Henon map as well as population data of the Canadian lynx. Simple three-layer forwardfeed networks, trained on a small sample data set, provided reasonably good fit to the data and performed well on short-term predictions. Simple neural network models trained on small data sets can perform quite well with synthetic chaotic series as well as population data. The accuracy of the predictions is comparable to that obtained using embedding techniques with three embedding dimensions or nonlinear regression models. In relation to embedding techniques, the current forecasting method using neural networks may be regarded as a global rather than a local method using d embedding dimensions with unit delay time. However, once the network has been trained, the calculation of predicted values is very fast and straightforward, without any searching as required by embedding methods