Predicting Geostationary 40–150 keV Electron Flux Using ARMAX (an Autoregressive Moving Average Transfer Function), RNN (a Recurrent Neural Network), and Logistic Regression: A Comparison of Models
Abstract We screen several algorithms for their ability to produce good predictive models of hourly 40–150 keV electron flux at geostationary orbit (data from GOES‐13) using solar wind, Interplanetary Magnetic Field, and geomagnetic index parameters that would be available for real time forecasting....
Saved in:
Main Authors: | L. E. Simms, N. Yu. Ganushkina, M. Van derKamp, M. Balikhin, M. W. Liemohn |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2023-05-01
|
Series: | Space Weather |
Subjects: | |
Online Access: | https://doi.org/10.1029/2022SW003263 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Drivers for Geostationary 2–200 keV Electron Fluxes as Observed at GOES Satellites
by: M. van deKamp, et al.
Published: (2024-08-01) -
Predicting Geostationary (GOES) 4.1–30 keV Electron Flux Over All MLT Using LEEMYR Regression Models
by: L. E. Simms, et al.
Published: (2024-08-01) -
Analysis of Features in a Sliding Threshold of Observation for Numeric Evaluation (STONE) Curve
by: Michael W. Liemohn, et al.
Published: (2022-06-01) -
Functional Prediction of Hypothetical Proteins from and Validation of the Predicted Models by Using ROC Curve Analysis
by: Md. Amran Gazi, et al.
Published: (2018-12-01) -
EM-AUC: A Novel Algorithm for Evaluating Anomaly Based Network Intrusion Detection Systems
by: Kevin Z. Bai, et al.
Published: (2024-12-01)