# Augmented Data and XGBoost Improvement for Sales Forecasting in the Large-Scale Retail Sector
## 1 Introduction  
The large-scale retail trade has now established itself internationally for decades. The spread of this modern retail system is associated with a society characterized by a strong economy. Despite this, when a country's economy slows, this sector immediately suffers as sales drop. In this scenario, it is of great importance to have an innovative method that makes it possible to predict sales whether the trend of the economy is negative, steady, or even positive. Machine learning has tools capable of modeling business strategies taking into account consumer purchasing patterns. Machine learning is that branch of artificial intelligence that deals with algorithms capable of extracting useful information from the shapeless mass of data [1]. Machine learning algorithms learn from data in the training phase and are thus able to make predictions on experimental data [2, 3]. Data mining (DM) combines the methodologies of machine learning and statistics in order to extract useful information from large amounts of data, such as databases and data warehouses [4]. DM uses artificial neural networks, classification trees, k-Nearest neighbor, Naive Bayes, and logistic regression to analyze data and generate classifications [5, 6, 7]. Data mining tools are also used by Business Intelligence (BI), which includes various processes and technologies used to collect, analyze, and make data usable in order to make better decisions [8]. BI also makes use of frontal analysis (dashboard), Executive Information System (EIS), and IT tools intended to support management activities, i.e., Decision Support System (DSS). BI performs a support function for business management with the aim of contributing to the qualitative improvement and greater speed of the organization'sdecision-making processes which can, therefore, increase its competitiveness [9; 10]. The ultimate goal of BI is to provide support for decision making. Its functions allow information to be collected, processed, and used to support decision making [11]. In particular, in large-scale retail sector, the main activities of BI are: analysis of store layout efficiency, customer loyalty, warehouse management, and sales prediction. The analysis of sales data allows for the prediction of market trends and helps companies to adopt adequate strategies to increase sales by acquiring greater competitiveness. A very powerful tool that is gaining ground in several areas is extreme gradient boosting (XGBoost). In fact, based on historical data, it is able to make precise predictions for diverse applications. In [12] XGBoost algorithm was used in order to obtain weather predictions, such as medium-term precipitation prediction, while the authors of [13] used the XGBoost approach for predicting the short-term power consumption in rural and residential areas. The research studies [14; 15] use an XGBoost-based model for deterministic wind power forecasting. The study [16] proposed a forecasting model based on a XGBoost--Long Short Term Memory (LSTM) algorithm in order to predict short-term and long-term sales changes. Similarly, the authors of [17] used XGBoost algorithms within a model they developed for Sales Time Series Forecasting. The authors of [18; 19] combine cluster analysis and an XGBoost algorithm to calculate the probability of different symptoms in young hypertensive patients and to predict heart disease, respectively. Finally, the research studies [20; 21; 22] propose different approaches based on an XGBoost technique for predicting, respectively, the onset of diabetes, crude oil prices, and urban fire accidents, which is useful for public safety. Different studies have been performed in Global Distribution System (GDS) and food factories, defining by DM algorithms Key Performance Indicators (KPIs) [23], product facing BI [9], quality processes [24; 25], and sales prediction [26]. Concerning sales prediction, different DM algorithms can be adopted [27], including weather effect [28] and social trends [29].