Abstract:
Sales forecasting is vital to supply chain management and operations between retailer and manufacturers in the retail industry. The abundant growth of digital data has minimized the traditional system and approaches to a specific tasks. Sales forecasting is the most challenging task for the retail industry's inventory management, marketing, customer service, and business financial planning. In this paper, we performed a predictive analysis of retail sales of the 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 is obtained from Citadel POS (Point Of Sale) from 2013 to 2018, a cloud-based application that facilitates retail stores to carry out transactions, manage inventories, customers, vendors, view reports, manage reports, manage sales, and tender data locally. The results show that Xgboost outperformed time series and other regression models and achieved the best performance with an MAE of 0.516 and RMSE of 0.63.
Keywords:
LSTM
,
Machine learning
,
Regression
,
ARIMA
,
Time Series
,
gradient boosting
,
Random
,
Sales Forecasting