Ensemble methodology for demand forecasting

Generating forecast is undoubtedly one of the most important sector in any industry. It not only helps the demand planner of the company which is focusing primarily on the future forecasting of the sales but also the inventory management and its handling cost. Down the line of the companys supply chain, a wrong forecast can impact in an alternate way of incurring heavy cost. Apart from this a correct forecasting leading to a good accuracy says how the profit and loss of the company depends upon. This heavily depends upon the predicting machine that the company is using for the forecasting. Demand planners look for a consistently accurate results over a period of time. Where the accuracy of the forecast is not only good but also the algorithm is stable in a long period of time. This we have illustrated through the analysis and the model takes care of the stability of a particular algorithm selected for a SKU. In this paper we want to highlight a new ensemble technique using the averaging method which not only gives priority to the algorithm which consistently maintains a good accuracy but also decreases the deviation from the actual sales. Based upon the history it tries to give the importance to the one which predicts better and penalizes other algorithms which deviate from the actual sales.