Multilayer feedforward networks are universal approximators
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Approximation capabilities of multilayer feedforward networks
Neural Networks
Machine Learning
A simulation study of artificial neural networks for nonlinear time-series forecasting
Computers and Operations Research
An investigation of neural networks for linear time-series forecasting
Computers and Operations Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regression neural network for error correction in foreign exchange forecasting and trading
Computers and Operations Research
Expert Systems with Applications: An International Journal
Bagging for Gaussian process regression
Neurocomputing
An artificial neural network (p,d,q) model for timeseries forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Recurrent neural networks and robust time series prediction
IEEE Transactions on Neural Networks
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The combination of forecasts resulting from an ensemble of neural networks has been shown to outperform the use of a single ''best'' network model. This is supported by an extensive body of literature, which shows that combining generally leads to improvements in forecasting accuracy and robustness, and that using the mean operator often outperforms more complex methods of combining forecasts. This paper proposes a mode ensemble operator based on kernel density estimation, which unlike the mean operator is insensitive to outliers and deviations from normality, and unlike the median operator does not require symmetric distributions. The three operators are compared empirically and the proposed mode ensemble operator is found to produce the most accurate forecasts, followed by the median, while the mean has relatively poor performance. The findings suggest that the mode operator should be considered as an alternative to the mean and median operators in forecasting applications. Experiments indicate that mode ensembles are useful in automating neural network models across a large number of time series, overcoming issues of uncertainty associated with data sampling, the stochasticity of neural network training, and the distribution of the forecasts.