Prediction horizon effects on stochastic modelling hints for neural networks
R. Drossu, Zoran Obradović · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1995
The objective of this paper is to investigate the relationship between stochastic models and neural network (NN) approaches to time series modelling. Experiments on a complex real life prediction problem (entertainment video traffic) indicate that prior knowledge can be obtained through stochastic analysis both with respect to an appropriate NN architecture as well as to an appropriate sampling rate, in the case of a prediction horizon larger than one. An improvement of the obtained NN predictor is also proposed through a bias removal post-processing, resulting in much better performance than the best stochastic model.