Figure 5 MAE improvements of ADL-intra-all and ADL-inter-all over ADL-own Categories such as Cold cereal, Salty snacks and Soup achieve limited forecasting improvements from extra information. In the category Yogurt and Milk, however, both models improve the forecasts over the ADL-own model significantly. In order to show the value of intra- and inter-category information at category level, in Figure 5, we illustrate the MAE improvements of ADL-intra-all and ADL-inter-all over among different categories. In categories, such as Frankfurters, Margarine/Butter, Carbonated beverages, Milk and Peanut butter, the contribution from intercategory information is relatively large, ranging from 12% to 78%, compared with that in other categories. For Mayonnaise Coffee and Cold cereal, including the intercategory information in the model could even worsen the forecasts. An explanation is that the useful predictors from other categories may be too weak to compensate for the loss by including the extra volume of noisy information from these categories. In order to investigate whether the proposed LASSO Granger is an effective way to identify the category level interactive structure, we compare the forecasting results of the proposed LASSO Granger with the results of a fully connected structure based on under rolling scheme. The full connection means that when forecasting the sales of SKUs in one category, all other 14 categories are considered as influential categories. The comparison results are illustrated in Figure 6. All the MAEs of LASSO Granger across categories are smaller or equal to that of using structure of full connections. This means that connections (Figure 3) identified by LASSO Granger enhance the model’s forecasting abilities by reducing the redundant noisy data for some categories. Figure 6 Forecasting comparisons between Full connection and LASSO Granger Table 5 one week ahead forecasting comparison between one-stage and three-stage LASSO Scheme MPE Model Estimation MAE RMSE MASE AvgRelMAE ADL-inter-top5 one-stage fixed 7.024 15.024 0.783 1.013 5.04
Figure 5 MAE improvements of ADL-intra-all and ADL-inter-all over ADL-own Categories such as Cold cereal, Salty snacks and Soup achieve limited forecasting improvements from extra information. In the category Yogurt and Milk, however, both models improve the forecasts over the ADL-own model significantly. In order to show the value of intra- and inter-category information at category level, in Figure 5, we illustrate the MAE improvements of ADL-intra-all and ADL-inter-all over among different categories. In categories, such as Frankfurters, Margarine/Butter, Carbonated beverages, Milk and Peanut butter, the contribution from intercategory information is relatively large, ranging from 12% to 78%, compared with that in other categories. For Mayonnaise Coffee and Cold cereal, including the intercategory information in the model could even worsen the forecasts. An explanation is that the useful predictors from other categories may be too weak to compensate for the loss by including the extra volume of noisy information from these categories. In order to investigate whether the proposed LASSO Granger is an effective way to identify the category level interactive structure, we compare the forecasting results of the proposed LASSO Granger with the results of a fully connected structure based on under rolling scheme. The full connection means that when forecasting the sales of SKUs in one category, all other 14 categories are considered as influential categories. The comparison results are illustrated in Figure 6. All the MAEs of LASSO Granger across categories are smaller or equal to that of using structure of full connections. This means that connections (Figure 3) identified by LASSO Granger enhance the model’s forecasting abilities by reducing the redundant noisy data for some categories. Figure 6 Forecasting comparisons between Full connection and LASSO Granger Table 5 one week ahead forecasting comparison between one-stage and three-stage LASSO Model Estimation Scheme MAE RMSE MASE AvgRelMAE MPE ADL-inter-top5 one-stage fixed 7.024 15.024 0.783 1.013 5.04