| Summary: | The concept of combined forecasting was first introduced by Bates and Granger (1969), and considering the fact that they can be based on different data and information, the forecasts that are combined can a very good way to improve the accuracy of the forecasting. According to research, combined forecasting has proved to provide very good results in the areas of expert forecasting and econometric forecasting (Armstrong, 2002) and has proven its usefulness in when difficult to select the most accurate forecasting method. Considerable literature has accumulated over the years regarding the combination of forecasts. The primary conclusion of this line of research is that forecast accuracy can be substantially improved through the combination of multiple individual forecasts. Furthermore, simple combination methods often work reasonably well relative to more complex combinations (Clemen, 1989). Linear programming is a mathematical technique for solving a broad class of optimization problems. These problems usually require the maximization or minimization of a linear function of variables subject to certain constrains (Nahmias, 2005). Linear programming (LP) has been applied extensively in the areas of business and operations management. However little has been in exploring the use of linear programming as a forecasting tool. Additionally, the hypothesis that the combination of various forecasting methods improves forecasting accuracy is also tested.
The aim of this dissertation is to examine the applicability of linear programming to combine forecasting, analyse the performance of this and compare this tool with the traditional approaches that are found in the literature. Additionally, the hypothesis that the combination of various forecasting methods improves forecasting accuracy is also tested.
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