  We enhance the accuracy and generalization of univariate time series point
prediction by an explainable ensemble on the fly. We propose an Interpretable
Dynamic Ensemble Architecture (IDEA), in which interpretable base learners give
predictions independently with sparse communication as a group. The model is
composed of several sequentially stacked groups connected by group backcast
residuals and recurrent input competition. Ensemble driven by end-to-end
training both horizontally and vertically brings state-of-the-art (SOTA)
performances. Forecast accuracy improves by 2.6% over the best statistical
benchmark on the TOURISM dataset and 2% over the best deep learning benchmark
on the M4 dataset. The architecture enjoys several advantages, being applicable
to time series from various domains, explainable to users with specialized
modular structure and robust to changes in task distribution.
