Using ANNs and ARIMA Models to Make Accurate Forecasts for Palestinian Official Statistics Based on Simulation and Empirical Applications

Samir K. Safi


Accuracy of forecasts of economic indicators is a major concern of statistical and economics departments. Over the past three decades there has been growing literature on applications of artificial neural networks (ANNs) to business and financial domains. ANNs do not assume restrictions during the modeling process because ANNs recognize the relationships between the variables. Thus, ANNs have the capability of executing the forecasting for different types of models without a pre- knowledge about the relationship between explanatory and response variable. In this paper, we demonstrate how ANNs can be used to make forecasts of artificial and real data sets. Furthermore, we will compare the accuracy of the ANN forecasts to those obtained by more classical time series models as autoregressive integrated moving average (ARIMA), using exhaustive simulation and real data on size of the population in the Palestinian Territories.


Population, Artificial Neural Networks, ARIMA, Forecasting, Time Series.

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