The Efficiency of Artificial Neural Networks for Forecasting in the Presence of Autocorrelated Disturbances
| dc.contributor.author | White, Alexander | |
| dc.contributor.author | Safi, Samir | |
| dc.date.accessioned | 2019-12-16T13:14:13Z | |
| dc.date.available | 2019-12-16T13:14:13Z | |
| dc.date.issued | 2016-02 | |
| dc.description.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. | |
| dc.description.department | Mathematics | |
| dc.format | Text | |
| dc.format.extent | 8 pages | |
| dc.format.medium | 1 file (.pdf) | |
| dc.identifier.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. | |
| dc.identifier.doi | https://doi.org/10.5539/ijsp.v5n2p51 | |
| dc.identifier.issn | 1927-7032 | |
| dc.identifier.uri | https://hdl.handle.net/10877/9084 | |
| dc.language.iso | en | |
| dc.publisher | Canadian Center of Science and Education | |
| dc.rights.license | This work is licensed under a Creative Commons Attribution 3.0 Unported License. | |
| dc.source | International Journal of Statistics and Probability, 2016, Vol. 5, No. 2, pp. 51–58. | |
| dc.subject | time series | |
| dc.subject | regression | |
| dc.subject | artificial neural networks | |
| dc.subject | Mathematics | |
| dc.title | The Efficiency of Artificial Neural Networks for Forecasting in the Presence of Autocorrelated Disturbances | |
| dc.type | Article |