## 1 Introduction  
Maximizing profits is a key objective for retail stores like supermarkets and convenience stores, and avoiding missed sales opportunities is crucial to achieving this goal. Monitoring on-shelf availability is essential for boosting profits, as product shortages, also known as out-of-stock (OOS) situations, are a major cause of revenue loss [1]. Formally, OOS events occur when a product requested by customers is not available on the market store shelves, and are addressed when the product(s) is received at the market store [2]. Crisis prediction has been used in the banking sector, as well as in business, investments, and other areas. Crisis prediction is critical for the financial market, and has attracted many researchers and academicians [3]. Stock markets were normally predicted by financial experts in the past. However, data scientists have started solving prediction problems with the progress of learning techniques [4]. Numerous approaches and different data can be used to address the problem of OOS detection [5]. To enhance profitability, retail establishments must focus on minimizing missed sales opportunities. On-shelf availability,which refers to the availability of products for purchase in their expected location and at the desired time, is a significant indicator of lost sales opportunities [6]. When customers find a desired product out of stock, they may opt to purchase a similar item from a competitor, resulting in lost revenue for the store [7; 8; 9]. Having products readily available on shelves is essential for boosting profits in retail stores. To further increase profitability, it is important to place products in areas that are easily seen by shoppers, such as at the front of shelves, rather than towards the back [10]. The commercial sector is experiencing new challenges and opportunities as a result of advancements in modern technology [11]. In particular, the use of recent predictive technologies grounded in deep learning is improving the ability to effectively address the issue of OOS products that are not available for purchase when requested by customers [12; 13; 14]. Reliable prediction systems accurately anticipate and manage instances of product unavailability, ensuring adequate shelf stocking to meet customer demand. The occurrence of an OOS event arises when a customer's desired product is unavailable in-store and is resolved once the replenishment of the product(s) is completed [15]. During this specific time frame, out-of-stock events lead to economic losses based on the level of product demand. Despite efforts by large companies to minimize the impact of out-of-stock events, their repercussions on the retail industry remain significant [16]. Studies suggest that around 4% of the total annual revenue in the commercial sector is lost as a result of out-of-stock occurrences, leading to more than USD 900 billion in financial losses. Some examples of unexpected market behavior are educational webinars, persona-based eBook marketing, and the use of branded schedule magnets. The study referenced found that out-of-stock events distort actual commercial demand, affecting brand loyalty and market dynamics. This method takes into account a process with a large number of connections in a multi-stage and parallel assembly. Priority relationships are established between the assembly units, and the assembly sequence is represented by a directed acyclic graph. Originally, a two-part crossover and mutation operators for assembling sequences were proposed. Due to the complexity of the assembly sequence planning problem, its optimization is required in order to obtain an efficient computational approach [17]. Utilizing deep learning is the next step in improving prediction models and achieving better performance. Even with advancements, predicting the stock market remains a difficult job for data scientists because of the market's intricate and unpredictable nature, as well as the connection between investors' emotions and market movements.