  We introduce a framework to dynamically combine heterogeneous models called
\texttt{DYCHEM}, which forecasts a set of time series that are related through
an aggregation hierarchy. Different types of forecasting models can be employed
as individual ``experts'' so that each model is tailored to the nature of the
corresponding time series. \texttt{DYCHEM} learns hierarchical structures
during the training stage to help generalize better across all the time series
being modeled and also mitigates coherency issues that arise due to constraints
imposed by the hierarchy. To improve the reliability of forecasts, we construct
quantile estimations based on the point forecasts obtained from combined
heterogeneous models. The resulting quantile forecasts are coherent and
independent of the choice of forecasting models. We conduct a comprehensive
evaluation of both point and quantile forecasts for hierarchical time series
(HTS), including public data and user records from a large financial software
company. In general, our method is robust, adaptive to datasets with different
properties, and highly configurable and efficient for large-scale forecasting
pipelines.
