International Journal of Statistics and Probability; Vol. 5, No. 2; 2016 ISSN 1927-7032 E-ISSN 1927-7040 Published by Canadian Center of Science and Education 51 The Efficiency of Artificial Neural Networks for Forecasting in the Presence of Autocorrelated Disturbances Alexander K White 1 & Samir K Safi 1,2 1 Texas State University, San Marcos, Texas, USA. 2 The Islamic University of Gaza, Palestine Correspondence: Samir K Safi, Texas State University, San Marcos, Texas, USA. samirsafi@gmail.com Received: January 22, 2016 Accepted: February 10, 2016 Online Published: February 14, 2016 doi:10.5539/ijsp.v5n2p51 URL: http://dx.doi.org/10.5539/ijsp.v5n2p51 Abstract We compare three forecasting methods, Artificial Neural Networks (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. Keywords: Artificial Neural Networks, Time Series, Regression 1. Introduction A good forecasting model is a key component to proper planning. Many different approaches exist for developing the forecast model, each designed to address special situations which arise in the time series. In this paper, we compare two traditional methods: Linear Regression and Autoregressive Integrated Moving Average to Artificial Neural Networks (ANNs). Given the complex contexts in which time series arise, there is a need for robust forecasting model which is flexible enough to be of use in a variety of situations. Previous research indicates that ANNs may provide such approach, see for example (Potočnik, et al. 2015, Adhikari & Agrawal, 2014, Thielbar & Dickey, 2011, Khashei & Bijari, 2010, Aksoy & Dahamsheh, 2009, Yasdi, 1999, among many others). Neural networks are the preferred tool for many predictive data mining applications because of their flexibility, power, accuracy and ease of use. The statistical methods assume that data are linearly related and therefore is not true in real life applications. The newly introduced method, the ANN which is inherently a nonlinear network and does not make such assumption, therefore is well suited for prediction purpose. (Safi, 2013). We use a data set of unemployment rates from Palestinian Central Bureau of Statistics (PCBS). The dataset contains the quarterly unemployment rates in Palestine during the period of the first quarter of 2000 through the second quarter of 2015. R-statistical software is used for fitting ANN, ARIMA, and regression models for the unemployment rates time series data. In this paper, ANN, ARIMA and regression models have been conducted for unemployment rates forecasting in Palestine. The main purpose of this paper is to find a more accurate and reliable forecasting model for the unemployment rates in Palestine. This paper is organized as follows: Section 2 presents review of ANN literature; in section 3, we present the comprehensive computer simulation results. Section 4 displays three forecasting cases fitting ARIMA, ANN, and Regression models for unemployment data in Palestine; and section 5 concludes some important results of this paper and offers future research. 2. Review of ANN Literature Artificial neural networks (ANN) have received a great deal of attention over the last years. They are being used in the areas of prediction and classification, areas where regression and other related statistical techniques have traditionally been used. (Cheng & Titterington, 1994). Box, et al. (1995) have developed the integrated autoregressive moving average (ARIMA) methodology for fitting a class of linear time series models. However, the statistical methods assume that data are linearly related and which is typically not true in real life applications. The newly introduced method, ANN, has emerged to be popular as it does not make such assumptions. The ANN, which is inherently a nonlinear network and does not make such assumptions, is