Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach
Adaptive robust optimization builds time-varying linear ensembles of forecasting models that reduce error by 16, 26% and risk by 14, 28% versus best single members and competing methods.
Published 2026Sydney Poster Session 4 · Wed, Dec 9, 5:00 PM–8:00 PM local time · Hall 1-4arXiv ↗OpenReview ↗
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Abstract
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.