Forecasting hierarchical time series for foodborne disease outbreaks

In this paper, we investigate hierarchical time series forecasting that adhere to a hierarchical structure when deriving predicted values by analyzing segmented data as well as aggregated datasets.The occurrences of food poisoning by a specific pathogen are analyzed using zero-inflated Poisson regression models and negative binomial regression models.The occurrences of major, miscellaneous, and overall food poisoning are analyzed using Poisson regression models and negative binomial regression models.For hierarchical time series forecasting, the MinT estimation proposed by Wickramasuriya et al. ( 2019) is employed.Negative predicted values resulting from hierarchical adjustments are adjusted to zero, and weights are multiplied to the remaining lowest-level variables to satisfy the hierarchical structure.Empirical analysis revealed that there is little difference between hierarchical and non-hierarchical adjustments in predictions based on pathogens.However, hierarchical adjustments generally yield superior results for predictions concerning major, miscellaneous, and overall occurrences.Without hierarchical adjustment, instances may occur where the predicted frequencies of the lowest-level variables exceed that of major or miscellaneous occurrences.However, the proposed method enables the acquisition of predictions that adhere to the hierarchical structure.