Demand Forecasting in Retail Business Using the Ensemble Machine Learning Framework - A Stacking Approach
Keywords:
Stacking Ensemble, Multilayer Perceptron , Demand ForecastingAbstract
Demand forecasting is an integral component of organizational and supply chain operations. Its primary objective is to anticipate the future demand for products, thereby informing and refining strategic decisions related to inventory management. Despite the inherent complexities in achieving precise demand forecasts, many methodologies have been proposed for the establishment of efficient forecasting systems. Such methodologies encompass traditional statistical approaches, hybrid techniques, and advanced methodologies rooted in machine learning and deep learning. Scholarly investigations within demand forecasting indicate a growing preference for deep learning paradigms, especially when confronted with data characterized by multivariate attributes, high dimensionality, and unpredictable demand fluctuations. Given the research emphasis on the retail domain, a sector inherently marked by data that is both multivariate and possesses volatile demand characteristics, this study devised a Stacking Ensemble learner. A comparative assessment was subsequently conducted, evaluating this ensemble against a trained Multilayer Perceptron , a deep learning archetype. The evaluation utilized a historical sales dataset sourced from ten Walmart outlets across Texas, California, and Wisconsin. Evaluative metrics were employed to discern the forecasting proficiencies of the respective frameworks. The evaluation determined that the Stacking Ensemble model outperformed the Multilayer Perceptron in terms of accurate predictions.
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