A Framework for Evaluating Geomagnetic Indices Forecasting Models
Abstract The use of Deep Learning models to forecast geomagnetic storms is achieving great results. However, the evaluation of these models is mainly supported on generic regression metrics (such as the Root Mean Squared Error or the Coefficient of Determination), which are not able to properly capt...
Saved in:
Main Authors: | Armando Collado‐Villaverde, Pablo Muñoz, Consuelo Cid |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2024-03-01
|
Series: | Space Weather |
Subjects: | |
Online Access: | https://doi.org/10.1029/2024SW003868 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Comment on “Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification” by Abduallah et al. (2024)
by: Armando Collado‐Villaverde, et al.
Published: (2024-08-01) -
Neural Networks for Operational SYM‐H Forecasting Using Attention and SWICS Plasma Features
by: Armando Collado‐Villaverde, et al.
Published: (2023-08-01) -
Operational SYM‐H Forecasting With Confidence Intervals Using Deep Neural Networks
by: Armando Collado‐Villaverde, et al.
Published: (2024-10-01) -
Forecasting Occurrence and Intensity of Geomagnetic Activity With Pattern‐Matching Approaches
by: C. Haines, et al.
Published: (2021-06-01) -
Developing the LDi and LCi Geomagnetic Indices, an Example of Application of the AULs Framework
by: C. Cid, et al.
Published: (2020-01-01)