## 2 Related Works
### Studies about Demand Forecasting Models
The performance of five machine learning regression methods--random forest, extreme gradient boosting, gradient boosting, adaptive boosting, and artificial neural network algorithms--was examined in the study in comparison to a hybrid model--the RF-XGBoost-LR--that was suggested for sales forecasting of a retail chain, taking into account various forecasting accuracy parameters. A retail company's weekly sales data were taken into account while analyzing projections, and the hybrid RF-XGBoost-LR model was found to perform better than the other models when evaluated using a variety of performance criteria [20]. [PERSON] et al. (2022) aimed to increase the success of sales forecasting by using the data mining technique k-means and the best-worst method model in the sales forecasting of retail products. The researchers, who said that they increased the quality of clustering products with the k-means algorithm, showed that, with this new model, which they named SalesKBR, the sales forecasts had a reasonable level of accuracy, and they predicted with a small error rate of 27.12%. The authors suggested that more successful results can be obtained in different sectors with different models, which were limited because their studies were in the retail sector and the dataset was relatively small [21]. [PERSON] and [PERSON] (2022) presented a hybrid neural network sales forecasting model based on the voting-ensemble approach. They claimed that their experiments demonstrated the high accuracy of a hybrid model in sales forecasting, enabling businesses to optimize inventory management and enhance logistics benefits [22]. They also mentioned that [PERSON] (2013) asserted that the grey neural network based on a genetic algorithm can better forecast future changes in sales volumes compared to time series regression and a BP neural network model [23]. To estimate product demand in an e-commerce firm, [PERSON] and [PERSON] (2022) presented an optimized forecasting model: an extreme learning machine (ELM) model paired with the Harris Hawks optimization (HHO) algorithm. The suggested ELM-HHO model's performance was also compared to that of classic ELM, ELM auto-tuned using Bayesian Optimization (ELM-BO), a Gated Recurrent Unit (GRU)-based recurrent neural network, and Long Short-Term Memory (LSTM) recurrent neural network models. The results showed that the suggested technique outperforms the existing product demand forecasting models in terms of prediction accuracy, and it can be used in real-time to estimate future product demand based on sales data from the previous week [24]. The goal of the study of [PERSON] et al. (2023) was to create and test a model for forecasting online product sales over a wide range of online product types. A continuous Stochastic Fractal Search (SFS) approach for optimizing the parameters of a deep learning modified neural network (DLMNN) was described by the authors, along with an analysis of a time series dataset. A RMSE, a MAE, and a MBE comparison of the SFS-DLMNN methodology with other popular methods such as the PSO, WOA, BRNN, and GA models were carried out by the authors. The results showed that their machine-learning technique produced improved outcomes with lower values [25]. [PERSON] and [PERSON] (2023) offered a network model for enterprise sales forecasting based on a mix of enterprise sales forecasting from the standpoint of digital management and neural networks. To improve sales forecasting and supply chain management, the authors provided a hybrid learning method that combined seasonal mode and support vector regression analysis. This hybrid model can investigate time series features and gain more valuable information to create a network capable of extracting more discriminative features. The authors forecasted the sales of clothing product types that were sensitive to seasonal effects and had a long sales cycle and used seasonal factors to process the data.