Multi‐Model Assessment of PCA‐Informer Hybrid Model Against Empirical and Deep Learning Methods in TEC Forecasting
Abstract Accurate forecasting of the ionospheric state is crucial for various applications including remote sensing and navigation. Total electron content (TEC) is an important ionospheric parameter to reflect ionospheric state. Consequently, there is a great interest in the prediction of TEC. In th...
Saved in:
Main Authors: | Yang Lin, Hanxian Fang, Die Duan, Ding Yang, Hongtao Huang, Chao Xiao, Ganming Ren |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2025-04-01
|
Series: | Space Weather |
Online Access: | https://doi.org/10.1029/2024SW004018 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Large-Scale Completion of Ionospheric TEC Maps Using Machine Learning Models With Constraints Conditions
by: Qingfeng Li, et al.
Published: (2025-01-01) -
ED‐Autoformer: A New Model for Precise Global TEC Forecast
by: Jiawei Zhou, et al.
Published: (2025-06-01) -
Modeling and forecasting of TEC using subspace-based SSA-LRF-ANN model
by: J.R.K. Kumar Dabbakuti, et al.
Published: (2025-07-01) -
A Predictive Model of the Position of Plasmapause Based on Lunar Phase and Deep Learning Framework
by: Yajun Li, et al.
Published: (2025-07-01) -
A Novel Optimized Hybrid VMD-PCA-XGBoost Model for Forecasting Precipitation: Exemplified by the Beijing-Tianjin-Hebei Study Region in China
by: Qiaoli Kong, et al.
Published: (2025-01-01)