## 2 Related Works
### Studies about Demand Forecasting Models
Their suggested model had better accuracy for predicting the future sales demand of garment enterprises and boosted the model training speed and reaction time [26]. According to [PERSON] et al. (2023), forecasting methodologies are important in assisting decision-making in e-commerce. Deep learning approaches (namely deep neural networks) were utilized by the literature to measure the \"learning\" ability to extract important insights from data. When compared to other simpler neural networks (e.g., multilayer perceptron architectures), \"long memory\" neural networks (e.g., long short-term memory architectures) are offered as the best alternative by [27]. According to [PERSON] and [PERSON] (2023), deep learning methods have demonstrated better performance than conventional forecasting methods in energy demand. It is a fact that big data are essential to overcoming the obstacles of renewable energy integration, since smart grids produce large amounts of data. Thus, big data technologies are becoming more and more essential in load forecasting as electric vehicles are gradually being introduced [28]. In their review article, [PERSON] et al. (2023) also mentioned that, in comparison to other approaches, AI-based load forecasting techniques utilizing machine learning and neural network models in the energy industry have demonstrated the highest forecast performance, obtaining higher overall RMS and MAPE values [29]. [PERSON] et al. (2023) outlined the main functions of the AI-predicting inventory model and offered optimization recommendations for e-commerce enterprises. When compared to other models in the case company, the AI forecasting model XGBoost exhibited the highest predictive accuracy and significantly increased the accuracy of inventory forecasting [30]. [PERSON] and [PERSON] (2024) classified the forecasting demand methods in fashion products into two categories: qualitative and quantitative methods. While the qualitative methods include techniques such as expert judgment, Delphi, and analytic hierarchy process, the quantitative methods embrace statistical and heuristics-based methods (time series-based models, Bayesian method-based, heuristic-based, panel data-based models, and others); artificial intelligence (AI); machine learning (ML); and hybrid methods (fuzzy-based, NN-based, clustering and classification, and grey-based hybrid models). AI and ML-based forecasting methods are classified as neural networks (NNs); deep learning; convolutional neural networks (CNNs); evolutionary neural networks (ENNs); extreme learning machine (ELM); and classification (Bayesian networks, genetic algorithm, random forest, and k-nearest neighbor). According to the authors, the utilization of machine learning forecasting models to tackle the demand fluctuations in the fashion industry has been recognized as an area where ongoing research would have a significant impact on the business. As companies expand into new channels or markets, ML-based forecasting has helped them produce more accurate projections and increase consumer involvement [31]. [PERSON] et al. (2024) compared the forecasting performance of ML models, including AdaBoost, multilayer perceptron neural network, support vector regression (SVR), random forest (RF), lasso regression, gradient boost, and extreme gradient boost, by using historical power production data. They found that the random forest model performs the best when forecasting model performances are ranked according to skill score results. The optimized multilayer perceptron NN forecasting model is the least effective aside from the baseline models ([PERSON] et al., 2019). According to [PERSON] et al. (2024), the use of ML and AI approaches is becoming more prevalent in demand forecasting systems, which are constantly progressing and offer promising tools to increase forecasting accuracy ([PERSON] et al., 2019).