Intelligent Sales Forecasting System Using Arima, Sarima, and Xgboost Models

Sales forecasting plays a pivotal role in predicting future revenue by estimating product or service volumes within a specified time frame. Accurate sales forecasting drives strategic decisions, such as pricing, store allocation, ordering, and inventory management, benefiting all departments within an organization. However, many companies still face challenges due to disconnections between departments and inadequate sharing of information from sales forecasts. This can lead to misalignment between product marketing’s demand plans and sales quotas, resulting in excessive or insufficient inventory and inaccurate sales targets. To address these challenges, machine learning and artificial intelligence techniques, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal AutoRegressive Integrated Moving Average (SARIMA) model, and Extreme Gradient Boosting (XGBoost) algorithm, were adopted. This research focuses on developing an intelligent sales forecasting system using Python and TensorFlow. The system incorporates data preparation, model training, evaluation, and iterative refinement. The ARIMA, SARIMA, and XGBoost models are compared, and the XGBoost model outperforms the others in terms of RMSE (727.13), MSE (528718.04), and MAE (261.29). The research demonstrates the significance of accurate sales forecasting for seamless business operations, improved credibility in the market, and enhanced productivity. By continually refining the models and leveraging insights from user feedback, the system can adapt to changing business requirements and remain competitive in predicting future sales accurately.