# Boosted Ensemble Learning based on Randomized NNs for Time Series Forecasting

arXiv: https://arxiv.org/abs/2203.00980 · HTML: https://mlsanity.com/paper/2203.00980/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Snapshot Ensembles: Train 1, get M for free](https://mlsanity.com/paper/1704.00109/) (2017)
- [Horizontal and Vertical Ensemble with Deep Representation for Classification](https://mlsanity.com/paper/1306.2759/) (2013)
- [A Method of Generating Random Weights and Biases in Feedforward Neural Networks with Random Hidden Nodes](https://mlsanity.com/paper/1710.04874/) (2017)
- [Diversity and degrees of freedom in regression ensembles](https://mlsanity.com/paper/1803.00314/) (2018)
- [Generating Random Parameters in Feedforward Neural Networks with Random Hidden Nodes: Drawbacks of the Standard Method and How to Improve It](https://mlsanity.com/paper/1908.05864/) (2019)
- [An Interpretable Probabilistic Model for Short-Term Solar Power Forecasting Using Natural Gradient Boosting](https://mlsanity.com/paper/2108.04058/) (2021)
- [Randomized Neural Networks for Forecasting Time Series with Multiple Seasonality](https://mlsanity.com/paper/2107.01705/) (2021)
- [Ensembles of Randomized NNs for Pattern-based Time Series Forecasting](https://mlsanity.com/paper/2107.04091/) (2021)

## Built on by (most cited first, 1 of 1)

- [Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality](https://mlsanity.com/paper/2504.08940/) (2025)

## Abstract (arXiv)

Time series forecasting is a challenging problem particularly when a time series expresses multiple seasonality, nonlinear trend and varying variance. In this work, to forecast complex time series, we propose ensemble learning which is based on randomized neural networks, and boosted in three ways. These comprise ensemble learning based on residuals, corrected targets and opposed response. The latter two methods are employed to ensure similar forecasting tasks are solved by all ensemble members, which justifies the use of exactly the same base models at all stages of ensembling. Unification of the tasks for all members simplifies ensemble learning and leads to increased forecasting accuracy. This was confirmed in an experimental study involving forecasting time series with triple seasonality, in which we compare our three variants of ensemble boosting. The strong points of the proposed ensembles based on RandNNs are extremely rapid training and pattern-based time series representation, which extracts relevant information from time series.