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 … Show more
“…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.…”
Section: Othermentioning
confidence: 99%
“…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'.…”
Section: Othermentioning
confidence: 99%
“…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…”
The few applications used by Small and Medium Scale Enterprises (SMEs’) businesses lack efficiency and the appropriate intelligence to save them from price instability, inventory carrying costs, excess inventory, wrong decision making, inaccurate monitoring of stock levels, etc. through predictive analytics. The study explored various Artificial Intelligence Machine Learning (AI/ML) models and data structure array types that could be used with the day-to-day local weather conditions of low and high temperatures to predict market parameters and aid SMEs with predictive data to use for combating wrong decision-making, inaccurate business monitoring and excess inventory, etc. Among the ML models explored included sequential minimal optimisation, iterative reweighted least-squares, Fan-Chen-Lin support vector regression, linear regression newton method and multivariate linear regression Ordinary least squares for a multivariate linear regression and logistic regression. The models were compiled using visual C# and Accord.Net libraries. Multivariate linear regression Ordinary least squares models recorded the least predictive accuracy loss, for the test quantity prediction test samples, and varying acceptable square loss values, for usage in geo-localised mobile intelligent systems for SME predictions due to their favourable scores. The jagged array overall performed better than the multi-dimensional array on some time and space complexity tests. This work is contributing to the body of knowledge by evaluatively suggesting better data structures and ML models for building intelligent systems in Xamarin forms using C# and small data for the model training for applications in mobile phone systems that will aid SMEs’ in adjusting spending and sales targets.
“…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.…”
Section: Othermentioning
confidence: 99%
“…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'.…”
Section: Othermentioning
confidence: 99%
“…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…”
The few applications used by Small and Medium Scale Enterprises (SMEs’) businesses lack efficiency and the appropriate intelligence to save them from price instability, inventory carrying costs, excess inventory, wrong decision making, inaccurate monitoring of stock levels, etc. through predictive analytics. The study explored various Artificial Intelligence Machine Learning (AI/ML) models and data structure array types that could be used with the day-to-day local weather conditions of low and high temperatures to predict market parameters and aid SMEs with predictive data to use for combating wrong decision-making, inaccurate business monitoring and excess inventory, etc. Among the ML models explored included sequential minimal optimisation, iterative reweighted least-squares, Fan-Chen-Lin support vector regression, linear regression newton method and multivariate linear regression Ordinary least squares for a multivariate linear regression and logistic regression. The models were compiled using visual C# and Accord.Net libraries. Multivariate linear regression Ordinary least squares models recorded the least predictive accuracy loss, for the test quantity prediction test samples, and varying acceptable square loss values, for usage in geo-localised mobile intelligent systems for SME predictions due to their favourable scores. The jagged array overall performed better than the multi-dimensional array on some time and space complexity tests. This work is contributing to the body of knowledge by evaluatively suggesting better data structures and ML models for building intelligent systems in Xamarin forms using C# and small data for the model training for applications in mobile phone systems that will aid SMEs’ in adjusting spending and sales targets.
“…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.…”
Section: Application Of Machine Learning To Sales Predictionmentioning
confidence: 99%
“…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].…”
Organizations engaged in business, regardless of the industry in which they operate, must be able to extract knowledge from the data available to them. Often the volume of customer and supplier data is so large, the use of advanced data mining algorithms is required. In particular, machine learning algorithms make it possible to build predictive models in order to forecast customer demand and, consequently, optimize the management of supplies and warehouse logistics. We base our analysis on the use of the XGBoost as a predictive model, since this is now considered to provide the more efficient implementation of gradient boosting, shown with a numerical comparison. Preliminary tests lead to the conclusion that the XGBoost regression model is more accurate in predicting future sales in terms of various error metrics, such as MSE (Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error) and WAPE (Weighted Absolute Percentage Error). In particular, the improvement measured in tests using WAPE metric is in the range 15–20%.
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