  Accurate time series forecasting is critical for a wide range of problems
with temporal data. Ensemble modeling is a well-established technique for
leveraging multiple predictive models to increase accuracy and robustness, as
the performance of a single predictor can be highly variable due to shifts in
the underlying data distribution. This paper proposes a new methodology for
building robust ensembles of time series forecasting models. Our approach
utilizes Adaptive Robust Optimization (ARO) to construct a linear regression
ensemble in which the models' weights can adapt over time. We demonstrate the
effectiveness of our method through a series of synthetic experiments and
real-world applications, including air pollution management, energy consumption
forecasting, and tropical cyclone intensity forecasting. Our results show that
our adaptive ensembles outperform the best ensemble member in hindsight by
16-26% in root mean square error and 14-28% in conditional value at risk and
improve over competitive ensemble techniques.
