# A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning

arXiv: https://arxiv.org/abs/2104.11475 · HTML: https://mlsanity.com/paper/2104.11475/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 23 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [Forecasting with sktime: Designing sktime's New Forecasting API and Applying It to Replicate and Extend the M4 Study](https://mlsanity.com/paper/2005.08067/) (2020)
- [MetaBags: Bagged Meta-Decision Trees for Regression](https://mlsanity.com/paper/1804.06207/) (2018)

## Built on by (most cited first, 10 of 10)

- [AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2308.05566/) (2023)
- [Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach](https://mlsanity.com/paper/2304.04308/) (2023)
- [Stacking for Probabilistic Short-term Load Forecasting](https://mlsanity.com/paper/2406.10718/) (2024)
- [Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting](https://mlsanity.com/paper/2305.15786/) (2023)
- [A Scalable and Transferable Time Series Prediction Framework for Demand Forecasting](https://mlsanity.com/paper/2402.19402/) (2024)
- [FoodRL: A Reinforcement Learning Ensembling Framework For In-Kind Food Donation Forecasting](https://mlsanity.com/paper/2511.04865/) (2025)
- [Attention-Based Ensemble Pooling for Time Series Forecasting](https://mlsanity.com/paper/2310.16231/) (2023)
- [SenDaL: An Effective and Efficient Calibration Framework of Low-Cost Sensors for Daily Life](https://mlsanity.com/paper/2502.10460/) (2025)
- [Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality](https://mlsanity.com/paper/2504.08940/) (2025)
- [Multi-layer Stack Ensembles for Time Series Forecasting](https://mlsanity.com/paper/2511.15350/) (2025)

## Abstract (arXiv)

The contribution of this work is twofold: (1) We introduce a collection of ensemble methods for time series forecasting to combine predictions from base models. We demonstrate insights on the power of ensemble learning for forecasting, showing experiment results on about 16000 openly available datasets, from M4, M5, M3 competitions, as well as FRED (Federal Reserve Economic Data) datasets. Whereas experiments show that ensembles provide a benefit on forecasting results, there is no clear winning ensemble strategy (plus hyperparameter configuration). Thus, in addition, (2), we propose a meta-learning step to choose, for each dataset, the most appropriate ensemble method and their hyperparameter configuration to run based on dataset meta-features.