## 1 Introduction
Large datasets and machine learning advancements have led to the creation of several effective object detection algorithms. However, these algorithms typically necessitate detailed labeling of a significant quantity of data to train effectively in a different field [18]. Given the time-consuming nature of the annotation process, weak supervised learning methods and sparse data labeling have gained popularity recently. Various proposed solutions focus on tackling OOS issues from the manufacturer's standpoint. These solutions rely on point-of-sale data and other distinct characteristics identified through statistical analysis or machine learning methods. The methods usually talked about in research papers are statistical models and deep learning strategies, especially supervised ones, to figure out how likely it is that an OOS event will happen and how to manage product inventories well to avoid them [19]. However, these scientific approaches suffer a decline in effectiveness due to the unpredictability of factors that significantly impact OOS events, making them difficult to model based on previous data. In certain instances, the dynamics of OOS events exhibit novelty, and didn't utilize historical market data. Also, the existing predictive models for OOS events may not take into account all the random factors that impact this occurrence. This study suggests a new model to address the limitations experienced by current models [20]. The practical application of artificial intelligence methods is particularly important for products that are described by numerous and different parameters. This group includes automotive heat exchangers, which also have to meet specific exploitative requirements resulting from their working conditions. Constant artificial neural network parameters are utilized for network learning in the BroydenFletcher-Goldfarb-Shanno algorithm, in which the number of input neuron connections is 52, and the activation functions are linear, logistic, exponential, and hyperbolic [21]. The new model tackles challenges in a distinct way compared to current solutions by utilizing a special blend of BCHO, a deep temporal CNN, and LightGBM. Instead of solely depending on statistical models or shallow learning techniques like traditional methods, this model incorporates BCHO, which draws inspiration from natural behaviors, to enhance model training efficiency. With the inclusion of a deep temporal CNN, the model is able to detect complex temporal patterns in data, leading to a more detailed analysis of stock market dynamics. Moreover, the partnership with LightGBM allows for the efficient management of large datasets and sparse data labels, effectively overcoming the restrictions of data annotation requirements. Furthermore, the proactive approach to managing stocks made possible by the model allows for the improved forecasting and management of stockouts, leading to more precise and dependable predictions in stock market analysis. In general, the model presents a thorough and creative solution that tackles the obstacles in stock market forecasting by merging cutting-edge optimization strategies with deep learning technologies and effective data management abilities. The aim of this research is to increase the accuracy of stock market predictions by combining BCHO with LightGBM and a deep temporal CNN. BCHO improves model training by using unique reinforcement and evasion tactics inspired by natural behavior. Incorporating BCHO with a deep temporal CNN efficiently identifies temporal patterns in stock market data, enabling a more thorough analysis of market trends. Moreover, working with LightGBM allows the model to effectively handle large datasets and sparse data labels. With this integrated approach, the model intends to offer more precise and dependable predictions through proactive stock management techniques, ultimately overcoming the obstacles of stock market forecasting.