Hierarchical Forecasting: The Role of Information

In hierarchical forecasting, the process of forecast reconciliation transforms a set of base forecasts, which do not satisfy hierarchical aggregation constraints, into coherent forecasts that do satisfy those constraints. Traditional improvements due to reconciliation have been attributed to imposing aggregation constraints. However, when base forecasts are based on different information sets and historical data are available, additional gains may be achieved by combining the information contained in the base forecasts. We propose a new method, called the information combination (IComb) method, which combines the information content of forecasts during the reconciliation process using penalised regression. We provide simulation evidence on the role of information sets, distinct from aggregation constraints, in hierarchical time series forecasting and show that the IComb method produces superior results compared to traditional reconciliation approaches.