In the retail industry, demand forecasting is absolutely crucial for guaranteeing efficient inventory and supply chain control. Different artificial intelligence (AI) techniques have been used lately to improve forecasting performance. Demand fluctuation, seasonal patterns, and outside influences continue to create difficulties, though. Using
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In the retail industry, demand forecasting is absolutely crucial for guaranteeing efficient inventory and supply chain control. Different artificial intelligence (AI) techniques have been used lately to improve forecasting performance. Demand fluctuation, seasonal patterns, and outside influences continue to create difficulties, though. Using several machine-learning techniques Linear Regression, XGBoost, Random Forest, Decision Tree, Prophet, and LSTM this paper offers a comparative study to forecast product demand. A retail dataset obtained from Kaggle served as the basis for training and testing the forecasting models. The experimental results demonstrate that the LSTM model outperforms all others with accuracy, precision, recall, and F1-score of 92.31%, 92.31%, 100.00%, and 96.00%, respectively, followed by Prophet with 85.71%, 92.31%, 92.31%, and 92.31%, respectively, Decision Tree with 93.05%, 75.76%, 76.13%, and 75.94%, respectively, Random Forest with 91.99%, 66.86%, 88.08%, and 76.02%, respectively, XGBoost with 83.21%, 45.70%, 87.84%, and 60.12%, respectively, and Linear Regression with 60.67%, 25.46%, 89.75%, and 39.67%, respectively. These results verify that ensemble and deep learning models can greatly help retailers in raising operational efficiency and notably improve forecasting accuracy.
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