  Enhancing the robustness and accuracy of time series forecasting models is an
active area of research. Recently, Artificial Neural Networks (ANNs) have found
extensive applications in many practical forecasting problems. However, the
standard backpropagation ANN training algorithm has some critical issues, e.g.
it has a slow convergence rate and often converges to a local minimum, the
complex pattern of error surfaces, lack of proper training parameters selection
methods, etc. To overcome these drawbacks, various improved training methods
have been developed in literature; but, still none of them can be guaranteed as
the best for all problems. In this paper, we propose a novel weighted ensemble
scheme which intelligently combines multiple training algorithms to increase
the ANN forecast accuracies. The weight for each training algorithm is
determined from the performance of the corresponding ANN model on the
validation dataset. Experimental results on four important time series depicts
that our proposed technique reduces the mentioned shortcomings of individual
ANN training algorithms to a great extent. Also it achieves significantly
better forecast accuracies than two other popular statistical models.
