Prediction of Time Series Data Using Multiresolution-based BiLinear Recurrent Neural Network

A time series prediction scheme based on multiresolution-based bilinear recurrent neural network (MBLRNN) is proposed in this paper. The proposed predictor is based on the BLRNN that has been proven to have robust abilities in modeling and predicting time series. The learning process is further improved by using a multiresolution-based learning algorithm for training the BLRNN so as to make it more robust for long-term prediction of the time series. The proposed MBLRNN-based predictor is applied to the long-term prediction of time series. Experiments and results on the Mackey-Glass Series data and Sunspot Series data show that the proposed MBLRNN outperforms both the traditional multilayer perceptron type neural network (MLPNN) and the BLRNN in terms of the normalized mean square error (NMSE).