Adaptive Ensemble of XGBoost and LSTM for Temperature Forecasting

DOI

10.2991/978-2-38476-585-0_28How to use a DOI?

Keywords

Weather prediction; ensemble methods; temperature forecasting; adaptive weighting

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.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Mingcheng Ye
PY  - 2026
DA  - 2026/06/18
TI  - Adaptive Ensemble of XGBoost and LSTM for Temperature Forecasting
BT  - Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025)
PB  - Atlantis Press
SP  - 237
EP  - 246
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-2-38476-585-0_28
DO  - 10.2991/978-2-38476-585-0_28
ID  - Ye2026
ER  -