Sales Forecasting Using Machine Learning Algorithm in the Retail Sector

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ID: 313089
2024
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Abstract
The ability to predict future sales is essential for modern firms. The already difficult work of sales forecasting is made much more difficult by a lack of data, missing data values, or outliers. Regression has a stronger relationship with sales forecasting complexity than time series does. Intricate patterns in the dynamics of sales that also involve a range of risk factors can be discovered using machine learning algorithms and supervised machine learning techniques. A company's sales projections need to be correct for it to succeed. By utilizing a reliable sales projection model, businesses may identify potential risks and make smarter decisions. In this study, Rossmann sales data will be analyzed using the Extreme Gradient Boosting (XG-Boost), FB-Prophet, and autoregressive integrated moving average (ARIMA) prediction models. A corporation can reduce costs associated with excess inventory, make future plans, and boost profitability with the use of an accurate sales forecast. Therefore, the model needs to be assessed using statistical techniques like R2, RMSE, and MAE. To determine if models are more accurate at predicting sales, the results are employed.
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imported_1777058954_69ebc48abbf03 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Naeem Aslam
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/370
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