Forecast reconciliation of agricultural GHG emissions in Europe with fuzzy clustering

Abstract

Abstract We propose a novel statistical procedure which combines forecasting reconciliation techniques and unsupervised clustering algorithms to forecast annual greenhouse gases emissions at the territorial level. Specifically, we aim at predicting future emissions from the agricultural sector for Europe by aggregating bottom-level regional forecasts to the national and continental levels. Fuzzy clustering is used to define aggregations of bottom and middle-level series into clusters based on the similarity of emissions patterns; then, the fuzzy hierarchies are combined with the original administrative aggregation structure to improve the prediction accuracy of forecasting models. Empirical results show that fuzzy clustering aggregation, by leveraging uncertainty compared to crisp approaches, enhances forecast reconciliation accuracy at all levels of the hierarchy. Our approach offers a scalable solution to enhance forecast accuracy and reliability, particularly for short time series characterized by complex hierarchical structures.