# Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality

arXiv: https://arxiv.org/abs/2504.08940 · HTML: https://mlsanity.com/paper/2504.08940/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)
- [Forecast combinations: an over 50-year review](https://mlsanity.com/paper/2205.04216/) (2022)
- [A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning](https://mlsanity.com/paper/2104.11475/) (2021)
- [Forecast with Forecasts: Diversity Matters](https://mlsanity.com/paper/2012.01643/) (2020)
- [Diversity and degrees of freedom in regression ensembles](https://mlsanity.com/paper/1803.00314/) (2018)
- [Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting](https://mlsanity.com/paper/2212.09030/) (2022)
- [Short-term Load Forecasting Based on Hybrid Strategy Using Warm-start Gradient Tree Boosting](https://mlsanity.com/paper/2005.11478/) (2020)
- [Boosted Ensemble Learning based on Randomized NNs for Time Series Forecasting](https://mlsanity.com/paper/2203.00980/) (2022)

## Abstract (arXiv)

In this paper, we investigate meta-learning for combining forecasts generated by models of different types. While typical approaches for combining forecasts involve simple averaging, machine learning techniques enable more sophisticated methods of combining through meta-learning, leading to improved forecasting accuracy. We use linear regression, k-nearest neighbors, multilayer perceptron, random forest, and long short-term memory as meta-learners. We define global and local meta-learning variants for time series with complex seasonality and compare meta-learners on multiple forecasting problems, demonstrating their superior performance compared to simple averaging.