Implementation of recurrent neural network and boosting method for time-series forecasting

Ensemble methods used for classification and regression have been shown that they are superior than other methods, theoretically and empirically. Adapting this method on time-series prediction is done by using boosting algorithm. On boosting algorithm, recurrent neural networks (RNN) are generated, each for training on a different set of examples on time-series data, then the results for each of this base learners will be combined and resulting on a final hypothesis. The difference between our algorithm and the original algorithm is the introduction of a new parameter for tuning the boosting influence on given examples. Our boosting result is then tested on real time-series forecasting, using a natural dataset and function-generated time series. On the experiment result, it can be proved that ensemble method that we used is better than standard method, backpropagation through time for one step ahead time series prediction.