Modular neural networks for recursive collaborative forecasting in the service chain
Knowledge-Based Systems
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Neural Processing Letters
Research on sustainable development based on neural network
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A Hybrid Intelligent Morphological Approach for Stock Market Forecasting
Neural Processing Letters
Expert Systems with Applications: An International Journal
Prediction of railway passenger traffic volume by means of LS-SVM
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
A robust automatic phase-adjustment method for financial forecasting
Knowledge-Based Systems
A Morphological-Rank-Linear evolutionary method for stock market prediction
Information Sciences: an International Journal
Hi-index | 0.00 |
The focus of this paper is on combination of artificial neural-network (ANN) forecasters with application to the prediction of daily natural gas consumption needed by gas utilities. ANN forecasters can model the complex relationship between weather parameters and previous gas consumption with the future consumption. A two-stage system is proposed with the first stage containing two ANN forecasters, a multilayer feedforward ANN and a functional link ANN. These forecasters are initially trained with the error backpropagation algorithm, but an adaptive strategy is employed to adjust their weights during online forecasting. The second stage consists of a combination module to mix the two individual forecasts produced in the first stage. Eight different combination algorithms are examined, they are based on: averaging, recursive least squares, fuzzy logic, feedforward ANN, functional link ANN, temperature space approach, Karmarkar's linear programming algorithm (1984) and adaptive mixture of local experts (modular neural networks). The performance is tested on real data from six different gas utilities. The results indicate that combination strategies based on a single ANN outperform the other approaches