Model selection in reconciling hierarchical time series

Model selection has been proven an effective strategy for improving accuracy in time series forecasting applications. However, when dealing with hierarchical time series, apart from selecting the most appropriate forecasting model, forecasters have also to select a suitable method for reconciling the base forecasts produced for each series to make sure they are coherent. Although some hierarchical forecasting methods like minimum trace are strongly supported both theoretically and empirically for reconciling the base forecasts, there are still circumstances under which they might not produce the most accurate results, being outperformed by other methods. In this paper we propose an approach for dynamically selecting the most appropriate hierarchical forecasting reconciliation method and leading to more accurate coherent forecasts. The approach, which we call conditional hierarchical forecasting, is based on machine learning classification methods that use time series features to select the reconciliation method for each hierarchy. Moreover, it allows the selection to be tailored according to the accuracy measure of preference and the hierarchical level(s) of interest. Our results suggest that conditional hierarchical forecasting can lead to significantly more accurate forecasts than standard approaches, especially at lower hierarchical levels.