## 3 Proposed Methodology for Out-of-Stock Prediction Model Using BCHO-TCN LightGBM
### Pre-Processing  
The process of data cleansing involves identifying and correcting or removing incorrect or noisy data from a dataset. It typically focuses on detecting and replacing incomplete, inaccurate, irrelevant, or noisy data, or other inappropriate entries. The specific problems this research focuses on are as follows.  
* Removing irrelevant or duplicate data: Duplications can arise in the dataset when data are gathered from various sources, including scraped data and data received from multiple clients. This is a common occurrence in data collection and consolidation processes.
* Structural error fixing: Inaccuracies arise from the incorrect labeling of categories or classes. These discrepancies can also stem from unconventional naming practices, spelling errors, or improper capitalization.
* Missing values: Missing values are a common occurrence in datasets, often caused by data validation rules or errors in data collection. However, it is important to address these missing values as they can impact the overall quality of a model. If only a small number of values are missing, simple interpolation methods can be used to fill in the gaps.  
This research uses very thorough methods to clean the data in order to make sure that the dataset is of high quality and is reliable. To start, deduplication methods are used to get  
Figure 1: Architecture of the proposed out-of-stock prediction model.  
rid of any unnecessary or copied data, which helps in finding and removing redundant entries from different data sources. Structural errors, like inconsistencies in labeling and unusual naming conventions, are fixed through standardized processes to make sure that the categories and classes are consistent and accurate. Also, missing values are filled in using interpolation techniques to maintain the completeness and integrity of the dataset. By methodically tackling these obstacles, the data cleaning procedure improves the dataset for training models, resulting in more precise and dependable forecasts of out-of-stock occurrences in the retail setting.  
Deduplication: This is the process of removing duplicate data from a dataset. During a secure data deduplication process, a tool assesses the data to find and remove extra copies, allowing for the storage of a single instance.  
Standardization: Standardization in data processing involves converting data into a uniform format or scale for greater consistency and comparability. This entails updating the values of variables in a dataset to have a mean of zero and a standard deviation of one. By standardizing the variables, differences in scales are eliminated, making data interpretation and analysis more straightforward [5].  
Interpolation: Interpolation methods involve using certain approaches to predict the values of missing data points in a dataset by referencing the values of neighboring data points. These methods are utilized when there are holes or missing values in a dataset, with the goal of filling in these gaps without altering the overall properties of the data [3]. So, after the completion of data cleansing, the preprocessed output is denoted as \(K_{r}^{*}\).