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

arXiv: https://arxiv.org/abs/2304.04308 · HTML: https://mlsanity.com/paper/2304.04308/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 67 references that are in the map.
- First posted on arXiv 9 Apr 2023.

## Builds on (most cited first, 14 of 14)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Adaptive Conformal Predictions for Time Series](https://mlsanity.com/paper/2202.07282/) (2022)
- [Interactive Supercomputing on 40,000 Cores for Machine Learning and Data Analysis](https://mlsanity.com/paper/1807.07814/) (2018)
- [Integrated multimodal artificial intelligence framework for healthcare applications](https://mlsanity.com/paper/2202.12998/) (2022)
- [Evaluating time series forecasting models: An empirical study on performance estimation methods](https://mlsanity.com/paper/1905.11744/) (2019)
- [JuMP: A Modeling Language for Mathematical Optimization](https://mlsanity.com/paper/1508.01982/) (2015)
- [An Experimental Review on Deep Learning Architectures for Time Series Forecasting](https://mlsanity.com/paper/2103.12057/) (2021)
- [Holistic Robust Data-Driven Decisions](https://mlsanity.com/paper/2207.09560/) (2022)
- [Hurricane Forecasting: A Novel Multimodal Machine Learning Framework](https://mlsanity.com/paper/2011.06125/) (2020)
- [A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning](https://mlsanity.com/paper/2104.11475/) (2021)
- [Global Flood Prediction: a Multimodal Machine Learning Approach](https://mlsanity.com/paper/2301.12548/) (2023)
- [Combining Multiple Time Series Models Through A Robust Weighted Mechanism](https://mlsanity.com/paper/1302.6595/) (2013)
- [Reducing Air Pollution through Machine Learning](https://mlsanity.com/paper/2303.12285/) (2023)

## Built on by (most cited first, 2 of 2)

- [Changes by Butterflies: Farsighted Forecasting with Group Reservoir Transformer](https://mlsanity.com/paper/2402.09573/) (2024)
- [REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting](https://mlsanity.com/paper/2608.10149/) (2026)

## Abstract (arXiv)

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.