Empirical Comparisons and Evaluation of Univariate Time series Models for Forecasting Sales of Internet Browsing Tickets in a Typical Nigerian University

The purpose of this research paper is to carry out comparative analysis and evaluation of some univariate time series forecasting methods and their application for forecasting sales of internet browsing ticket. Applying weekly data spreading over October 2014 to November 2015 on the amount of money generated from the sales of internet browsing ticket in the Federal University of Technology, Akure, Nigeria. The forecasting performance of various forecasting methods were measured through the use of the following forecast accuracy criteria Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE), Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The forecasting methods analyzed included, nave method, moving average, double moving average and exponential smoothing. Among these methods, the moving average method performed better than other competing methods in the sales of Internet Browsing ticket. The moving average method produced the most accurate forecasting based on the data obtained in the ranking table, therefore, as a matter of policy implication, the moving average method is therefore recommended for use in the process of forecasting of sales of internet browsing ticket in Nigerias Higher Educational Institutions where internet browsing tickets are sold