The Efficiency of Artificial Neural Networks for Forecasting in the Presence of Autocorrelated Disturbances
Date
2016-02
Authors
White, Alexander
Safi, Samir
Journal Title
Journal ISSN
Volume Title
Publisher
Canadian Center of Science and Education
Abstract
We compare three forecasting methods, Artificial Neural Networks (ANNs) ANNs), Autoregressive Integrated Moving Average (ARIMA) and Regression models . Using computer simulations, the major finding reveals that in the presence of autocorrelated errors ANNs perform favorably compared to ARIMA and regression for nonlinear models. The model accuracy for ANN is evaluated by comparing the simulated forecast results with the real data for unemployment in Palestine which were found to be in excellent agreement.
Description
Keywords
time series, regression, artificial neural networks, Mathematics
Citation
White, A. K., & Safi, S. K. (2016). The efficiency of artificial neural networks for forecasting in the presence of autocorrelated disturbances. International Journal of Statistics and Probability, 5(2), pp. 51–58.
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This work is licensed under a Creative Commons Attribution 3.0 Unported License.