Advanced Methods for Time Series Prediction Using Recurrent Neural Networks

Time series prediction has important applications in various domains such as medicine, ecology, meteorology, industrial control or finance. Generally the characteristics of the phenomenon which generates the series are unknown. The information available for the prediction is limited to the past values of the series. The relations which describe the evolution should be deduced from these values, in the form of functional relation approximations between the past and the future values. The most usually adopted approach to consider the future values ( ) 1 t x + consists in using a function f which takes as input a time window of fixed size M representing the recent history of the time series. ( ) ( ) ( ) ( ) ( ) [ ] τ − − τ − = 1 M t x , , t x , t x t ... x (1) ( ) ( ) ( ) t f t x x = τ + (2)