Time series, Elliott waves, artificial neural networks, prediction.

We develop two models for analysis and forecasting of financial time series. The first model is based on Elliott waves, which disposes of fractal structure. The second one uses an artificial neural network that is adapted by backpropagation. The Elliott wave principle is a detailed description of how financial markets behave. Artificial neural networks are suitable for predicting time series because are able to generalize and are resistant to noise. This paper also includes experimental results of time series prediction carried out with both mentioned models.