A Predictive Analysis of Retail Sales Forecasting using Machine Learning Techniques

Abstract

In a retail industry, sales forecasting is an important part related to supply chain management and operations between the retailer and manufacturers. The abundant growth of the digital data has minimized the traditional system and approaches to do a specific task. Sales forecasting is the most challenging task for the inventory management, marketing, customer service and Business financial planning for the retail industry. In this paper we performed predictive analysis of retail sales of Citadel POS dataset, using different machine learning techniques. We implemented different regression (Linear regression, Random Forest Regression, Gradient Boosting Regression) and time series models (ARIMA LSTM), models for sale forecasting, and provided detailed predictive analysis and evaluation. The dataset used in this research work is obtained from Citadel POS (Point Of Sale) from 2013 to 2018 that is a cloud base application and facilitates retail store to carryout transactions, manage inventories, customers, vendors, view reports, manage sales, and tender data locally. The results show that Xgboost outperformed time series and other regression models and achieved best performance with MAE of 0.516 and RMSE of 0.63.