Augmented Data and XGBoost Improvement for Sales Forecasting in the Large-Scale Retail Sector

Abstract

The organized large-scale retail sector has been gradually establishing itself around the world, and has increased activities exponentially in the pandemic period. This modern sales system uses Data Mining technologies processing precious information to increase profit. In this direction, the extreme gradient boosting (XGBoost) algorithm was applied in an industrial project as a supervised learning algorithm to predict product sales including promotion condition and a multiparametric analysis. The implemented XGBoost model was trained and tested by the use of the Augmented Data (AD) technique in the event that the available data are not sufficient to achieve the desired accuracy, as for many practical cases of artificial intelligence data processing, where a large dataset is not available. The prediction was applied to a grid of segmented customers by allowing personalized services according to their purchasing behavior. The AD technique conferred a good accuracy if compared with results adopting the initial dataset with few records. An improvement of the prediction error, such as the Root Mean Square Error (RMSE) and Mean Square Error (MSE), which decreases by about an order of magnitude, was achieved. The AD technique formulated for large-scale retail sector also represents a good way to calibrate the training model.

Considerations of an efficiency-intelligent geo-localised mobile application for personalised SME market predictions

“…During the same study, forecast benchmark performances of different models and combinations of different models were established, for the results of the single models underperformed as compared to the other combined models overperforming in the time series investigations. 8 Massaro et al, 9 in the article, Augmented Data and XGBoost Improvement for Sales Forecasting in the Large-Scale Retail Sector reported improved results and reduced errors in the forecasts due to the use XGBoost model, trained and tested using Augmented Data (AD) technique. The method employed data mining technologies, processing precious information to increase sales profit while applying the extreme gradient boosting (XGBoost) algorithm in an industrial project as a supervised learning algorithm to predict product sales including promotion conditions and a multiparametric analysis.…”

“…The method employed data mining technologies, processing precious information to increase sales profit while applying the extreme gradient boosting (XGBoost) algorithm in an industrial project as a supervised learning algorithm to predict product sales including promotion conditions and a multiparametric analysis. 9 Applying deep learning models for stock price time series forecasting equally has shown promising results on the extremely nonlinear time series data which has been a challenge for a while. 10 Balaji et al, 10 pointed out that, 'Accurate prediction of stock prices and the direction of stock price movement is also essential for a stock trader/investor to trade profitably'.…”

“…The method employed data mining technologies, processing precious information to increase sales profit while applying the extreme gradient boosting (XGBoost) algorithm in an industrial project as a supervised learning algorithm to predict product sales including promotion conditions and a multiparametric analysis. 9…”

Developing and Preliminary Testing of a Machine Learning-Based Platform for Sales Forecasting Using a Gradient Boosting Approach

“…In the field of sales prediction, methodologies based on approaches that implement boosting algorithms are of particular importance, due to the accuracy of the predictions [18][19][20]. In one study [21], an extreme gradient boosting (XGBoost) algorithm is used to implement a predictive model applied to the forecast of sales in the large-scale retail sector. The discussed method is tested on the prediction of various products and validated by comparing the predicted values with real data.…”

“…XGBoost is a scalable technology that optimizes the boosting concept underlying the GB algorithm [33]. This efficient algorithm allows the implementation of a predictor with excellent mathematical ability and with reduced computational costs [21,23]. XGBoost is effective and flexible due to the various hyperparameters [34].…”