Study of the Behavior of a New Boosting Algorithm for Recurrent Neural Networks

We present an algorithm for improving the accuracy of recurrent neural networks (RNNs) for time series forecasting. The improvement is achieved by combining a large number of RNNs, each of them is generated by training on a different set of examples. This algorithm is based on the boosting algorithm and allows concentrating the training on difficult examples but, unlike the original algorithm, by taking into account all the available examples. We study the behavior of our method applied on three time series of reference with three loss functions and with different values of a parameter. We compare the performances obtained with other regression methods.