Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910909617668096 |
|---|---|
| author | Dudek, Grzegorz |
| author_facet | Dudek, Grzegorz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08940 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality Dudek, Grzegorz Machine Learning Artificial Intelligence 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. |
| title | Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.08940 |