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.

翻译：准确的时间序列预测对于处理时序数据的广泛问题至关重要。集成建模是一种成熟的技术，通过利用多个预测模型来提高精度和鲁棒性，因为单个预测器的性能可能因底层数据分布的变化而高度波动。本文提出了一种构建鲁棒时间序列预测模型集成的新方法。我们的方法利用自适应鲁棒优化（ARO）来构建一个线性回归集成，其中模型的权重可以随时间自适应调整。我们通过一系列合成实验和实际应用（包括空气污染管理、能耗预测和热带气旋强度预测）证明了我们方法的有效性。结果表明，我们的自适应集成在均方根误差上比事后最优集成成员提高了16–26%，在条件风险价值上提高了14–28%，并且优于竞争性集成技术。