  In this work, we propose a novel framework for density forecast combination
by constructing time-varying weights based on time series features, which is
called Feature-based Bayesian Forecasting Model Averaging (FEBAMA). Our
framework estimates weights in the forecast combination via Bayesian log
predictive scores, in which the optimal forecasting combination is determined
by time series features from historical information. In particular, we use an
automatic Bayesian variable selection method to add weight to the importance of
different features. To this end, our approach has better interpretability
compared to other black-box forecasting combination schemes. We apply our
framework to stock market data and M3 competition data. Based on our structure,
a simple maximum-a-posteriori scheme outperforms benchmark methods, and
Bayesian variable selection can further enhance the accuracy for both point and
density forecasts.
