A Commentary on Demand Forecasting Using Machine Learning Technology

Henry Asante Antwi, Tevitta Tangaroa Vakalalabure


To date many tools and techniques have been developed to harness information and optimize them for effective forecasting and decision making. Despite the multiplicity of forecasting tools and techniques available, many forecast conducted today ends up in disappointments and significant errors. Most of them cannot easily identify and account for trends in demand data, and where the process is demand intensive, it suffer from persistent bias. However, new technologies such as machine learning have emerged also alternative efficient and reliable tools in demand forecasting within the last few years. This report examines and applies machine learning to demand forecasting and evaluates their strengths and weaknesses using two cases in point.

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