Abstract
Several reconciliation methods are developed to adjust base forecasts in a hierarchical framework to achieve coherence. In practice, it is common that the hierarchy of interest is large (i.e., high-dimensional), and the base forecasts of some series within the hierarchy are of poor quality due to intrinsic characteristics of the series (e.g., intermittence) or model misspecification. In this talk, I will formulate a reconciliation method with subset selection that eliminates the negative effects of some inferior base forecasts on the performance of the reconciled forecasts. Our method is expected to yield coherent forecasts that are comparable to or even better than those produced by state-of-the-art reconciliation methods, with fewer nodes being used.
Xiaoqian Wang
Assistant Professor
My research interests mainly include time series forecasting and big data analysis.