Hybrid neural network models for hydrologic time series forecasting
Applied Soft Computing
Computers and Electrical Engineering
A stratified model for short-term prediction of time series
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
A real-time multimedia data transmission rate control using a neural network prediction algorithm
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
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The artificial neural network (ANN) methodology has been used in various time series prediction applications. However, the accuracy of a neural network model may be seriously compromised when it is used recursively for making long-term multi-step predictions. This study presents a method using multiple ANNs to make a long term time series prediction. A multiple neural network (MNN) model is a group of neural networks that work together to solve a problem. In the proposed MNN approach, each component neural network makes forecasts at a different length of time ahead. The MNN method was applied to the problem of forecasting an hourly customer demand for gas at a compression station in Saskatchewan, Canada. The results showed that a MNN model performed better than a single ANN model for long term prediction.