A Comprehensive Study on Different Machine Learning Approaches for Retail Sales Forecasting: Methods, Procedures, Obstacles, and Prospects

Sales Forecasting for Retail segments should be a key concern for Businesses to ameliorate and emphasize operational efficiency, inventory optimization, and resource allocation. This research presents a detailed and systematic analysis of the application of machine learning methods for this purpose. Traditional methods may be effective, but they frequently struggle to keep pace with the subtle and ever-changing patterns underlying retail sales data. Machine learning algorithms have become better at tackling this problem in recent years and are generating more accurate sales forecast results. We investigate a diverse variety of machine learning models by studying literature from existing research papers and work for Statistical models and techniques, Univariate, and Multivariate time series forecasting models, deep learning methods and algorithms, regression techniques, neural networks, and XGBoost. In general, the anticipated result of this investigation will be useful to future scholars working in the field of sales forecasting. Notably, our accomplished results should facilitate assessment efficiency and memory consumption adroitly. Furthermore, this research surveys the hindrance of current approaches and crystalizes possible directions for delving deeper.