Using Time Series Models and Neural Networks to Predict the Sales of Motor Oils in Iraq

Abstract

Time series are the basis for statistical forecasting through building models that depend as a basis in economic analysis, and the most important of these models are Box-Jenkins models, which depends on the interpretation of the relationship between autocorrelation and partial autocorrelation, and when compared with the Artificial Neural Network (ANN) methodology which is considered as one of the new important means in building models, analyzing, evaluating data, forecasting and controlling it without referring to a common statistical model or method to pre-diagnose the behavior of the phenomenon, where the data is processed to reach the best model. This leads to the least possible number of errors and is close to reality and can be used in most cases. One of the goals of time series analysis is to obtain an accurate description of the time series, build a model to explain its behavior, and use the results for the purpose of predicting the series’ behavior in the future, and to achieve this, it requires an analytical study of time series models based on statistical and computer methods.