Multi-layer Stack Ensembles for Time Series Forecasting

Nathanael Bosch, Oleksandr Shchur, Nick Erickson, Michael Bohlke-Schneider, Ali Caner Turkmen

Confirmation: our paper adheres to reproducibility best practices. In particular, we confirm that all important details required to reproduce results are described in the paper,, the authors agree to the paper being made available online through OpenReview under a CC-BY 4.0 license (https://creativecommons.org/licenses/by/4.0/), and, the authors have read and commit to adhering to the AutoML 2025 Code of Conduct (https://2025.automl.cc/code-of-conduct/).

TL;DR: We show how to improve the accuracy of time series forecasting systems by combining multiple models into multi-layer stack ensembles.

Abstract: Ensembling is a powerful technique for improving the accuracy of machine learning models, with methods like stacking achieving strong results in tabular tasks. In time series forecasting, however, ensemble methods remain underutilized, with simple linear combinations still considered state-of-the-art. In this paper, we systematically explore ensembling strategies for time series forecasting. We evaluate 33 ensemble models—both existing and novel—across 50 real-world datasets. Our results show that stacking consistently improves accuracy, though no single stacker performs best across all tasks. To address this, we propose a multi-layer stacking framework for time series forecasting, an approach that combines the strengths of different stacker models. We demonstrate that this method consistently provides superior accuracy across diverse forecasting scenarios. Our findings highlight the potential of stacking-based methods to improve AutoML systems for time series forecasting.

Submission Number: 14