Adaptive Ensemble of XGBoost and LSTM for Temperature Forecasting

Mingcheng Ye ()
Additional contact information
Mingcheng Ye: Beijing Institute of Technology, School of Computer Science and Technology

A chapter in Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025), 2026, pp 237-246 from Springer

Abstract: Abstract Accurate weather prediction is crucial for numerous applications, ranging from daily decision-making to emergency response and disaster mitigation. This study introduces an adaptive ensemble method for temperature forecasting that integrates two distinct machine learning algorithms. The ensemble framework dynamically adjusts the weights of each individual model based on their performance characteristics, ensuring that the most reliable predictions are prioritized. The method was tested on historical weather data from five major European cities, consistently demonstrating superior performance compared to standalone models. The results show that the adaptive ensemble achieved R2 values exceeding 0.99 across all locations, indicating a high degree of predictive accuracy. Notably, the geographic location of each city significantly influenced the weight allocation within the ensemble, suggesting that spatially-dependent feature interactions play a more dominant role than temporal patterns in determining temperature variations in these regions. These findings highlight the potential of adaptive ensemble strategies in enhancing the robustness and precision of weather forecasting models across diverse climatic and geographical contexts.

Keywords: Weather prediction; ensemble methods; temperature forecasting; adaptive weighting (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-2-38476-585-0_28

Ordering information: This item can be ordered from
http://www.springer.com/9782384765850

DOI: 10.2991/978-2-38476-585-0_28

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().