Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach

• URL: http://arxiv.org/abs/2304.04308v1
• Date: Sun, 9 Apr 2023 20:30:10 GMT
• Title: Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach
• Authors: Dimitris Bertsimas, Leonard Boussioux
• Abstract summary: This paper proposes a new methodology for building robust ensembles of time series forecasting models. We demonstrate the effectiveness of our method through a series of synthetic experiments and real-world applications.
• Score: 3.7565501074323224
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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