Nonlinear combination of forecasts using artificial neural network, fuzzy logic and neuro-fuzzy approaches

In the actual practice, it becomes interesting from the efficiency point of view to combine various forecasts of a specific time series into a single forecast and to interrogate the resulting forecasting accuracy. The combination is usually nonlinear. Various intelligent combination techniques have been suggested for this purpose, based on different neural network architectures, including the feedforward neural network and evolutionary neural network. In this paper, the nonlinear combination of time series forecasts is proposed, based on isolated use of neural networks, fuzzy logic and neuro-fuzzy systems. On some practical examples it is demonstrated that the nonlinear combination of a group of forecasts based on intelligent approach is capable of producing a single better forecast than any individual forecasts involved in the combination