A Homogeneous Ensemble of Artificial Neural Networks for Time Series Forecasting
This work addresses forecasting accuracy issues for researchers and practitioners in time series analysis, but it is incremental as it builds on existing ANN training methods.
The paper tackles the problem of slow convergence and local minima in ANN training for time series forecasting by proposing a weighted ensemble scheme that combines multiple training algorithms, achieving significantly better forecast accuracies than two popular statistical models on four time series datasets.
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.