STORE SALES PREDICTION USING MACHINE LEARNING, STATISTICAL MODELING AND DEEP LEARNING FRAMEWORKS: A COMPARATIVE ANALYSIS
DOI:
https://doi.org/10.32350.umt-air.52.05Keywords:
Sales forecasting, Comparative analysis, Statistical modeling, Machine learning, Deep learningAbstract
Sales forecasting or prediction is an essential tool for businesses to make informed decisions about resource allocation, budgeting, and marketing strategies. Sales prediction involves predicting future sales of a product or service. There are several methods that businesses can use to forecast sales, including statistical analysis, market research, and expert opinion. Key factors that can impact sales predictions include market conditions, economic trends, competition, and customer behavior. However, forecasting sales can be a challenging tasks, as it require businesses to accurately forecast future demand and predict future sales in the face of changing market conditions and unpredictable customer behavior. There are also several uncertainties that businesses may face when forecasting or predicting sales, such as economic conditions, competition, customer behavior, product life cycles, and external events. In this research, we are doing sales prediction and providing a comparative analysis of three widely used techniques in the field of forecasting, namely: statistical modeling, machine learning, and deep learning. In our analysis, we are using the data given by the Rossmann supply chain, which is the second largest drugstore chain in Europe, and to the best of our knowledge, no one has ever used the deep learning approach to predict sales of these stores. Our study aims to forecast sales and improve the results of the prediction.
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