White, AlexanderSafi, Samir2019-12-162019-12-162016-02White, 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. 51–58.1927-7032https://hdl.handle.net/10877/9084We 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.Text8 pages1 file (.pdf)entime seriesregressionartificial neural networksMathematicsArticlehttps://doi.org/10.5539/ijsp.v5n2p51This work is licensed under a Creative Commons Attribution 3.0 Unported License.