Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach
This addresses the need for more reliable forecasts in domains like air pollution management and energy consumption, though it is an incremental improvement over existing ensemble techniques.
The paper tackles the problem of improving accuracy and robustness in time series forecasting by proposing an adaptive robust optimization approach to build ensembles, resulting in outperformance of the best ensemble member by 16-26% in RMSE and 14-28% in conditional value at risk.
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.