Neural network based fusion of global and local information in predicting time series

In the literature, an approach, Markov Fourier Grey model (MFGM) has been proposed to incorporate global information based on local prediction schemes. In traditional forecasting, people may want to predict the next data and this kind of prediction is called one-step prediction. Nevertheless, we may also need to make multi-step prediction. From our simulation, it can be found that local prediction schemes of MFGM can have nice performance in one-step prediction. However, they usually have awful performance for multi-step prediction. In this study, we study approaches in combining local and the global prediction results. Neural networks are widely used to predict time series. In our study, neural networks are employed as global prediction schemes and Fourier Grey Model (FGM) is employed as local prediction schemes. In the paper, we proposed a neural network based approach for the fusion of global and local information in predicting time series.