Recurrent networks

All the models that we have analyzed until now have a common feature. Once the training process is completed, the weights are frozen and the output depends only on the input sample. Clearly, this is the expected behavior of a classifier, but there are many scenarios where a prediction must take into account the history of the input values. A time series is a classic example. Let's suppose that we need to predict the temperature for the next week. If we try to use only the last known x^(t) value and an MLP trained to predict x^(t+1), it's impossible to take into account temporal conditions like the season, the history of the season over the years, the position in the season, and so on. The regressor will be able to associate the output that yields the minimum average error, but in real-life situations, this isn't enough. The only reasonable way to solve this problem is to define a new architecture for the artificial neuron, to provide it with a memory. This concept is shown...