A Nonlinear Autoregressive Approach to Statistical Prediction of Disturbance Storm Time Geomagnetic Fluctuations Using Solar Data

Journal of Signal and Information Processing

ISSN Print: 2159-4465
ISSN Online: 2159-4481
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"A Nonlinear Autoregressive Approach to Statistical Prediction of Disturbance Storm Time Geomagnetic Fluctuations Using Solar Data"
written by Joseph M. Caswell,
published by Journal of Signal and Information Processing, Vol.5 No.2, 2014

has been cited by the following article(s):

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[2]	Geomagnetic storm forecasting from solar coronal holes Monthly Notices of the Royal Astronomical Society, 2022 DOI:10.1093/mnras/stac3533

[3]	Prominence of the training data preparation in geomagnetic storm prediction using deep neural networks Scientific Reports, 2022 DOI:10.1038/s41598-022-11721-8

[4]	Geomagnetic storm forecasting from solar coronal holes Monthly Notices of the Royal Astronomical Society, 2022 DOI:10.1093/mnras/stac3533

[5]	Artificial NARX Neural Network Model of Wind Speed: Case of Istanbul-Avcilar Journal of Electrical Engineering & Technology, 2021 DOI:10.1007/s42835-021-00763-z

[6]	Disturbance Storm Time Index Prediction using Long Short-Term Memory Machine Learning 2021 4th International Conference of Computer and Informatics Engineering (IC2IE), 2021 DOI:10.1109/IC2IE53219.2021.9649119

[7]	Disturbance Storm Time Index Prediction using Long Short-Term Memory Machine Learning 2021 4th International Conference of Computer and Informatics Engineering (IC2IE), 2021 DOI:10.1109/IC2IE53219.2021.9649119

[8]	Initialization Approach for Decoupling Polynomial NARX Models Using Tensor Decomposition IFAC-PapersOnLine, 2020 DOI:10.1016/j.ifacol.2020.12.181

[9]	Initialization Approach for Decoupling Polynomial NARX Models Using Tensor Decomposition IFAC-PapersOnLine, 2020 DOI:10.1016/j.ifacol.2020.12.181

[10]	Statistical Study between Characteristic Parameters of Coronal Holes and Intensity/Time of Geomagnetic Storms Chinese Journal of Space Science, 2020 DOI:10.11728/cjss2020.01.009

[11]	Statistical Study between Characteristic Parameters of Coronal Holes and Intensity/Time of Geomagnetic Storms Chinese Journal of Space Science, 2020 DOI:10.11728/cjss2020.01.009

[12]	Artificial Neural Networks-Based Intrusion Detection System for Internet of Things Fog Nodes IEEE Access, 2020 DOI:10.1109/ACCESS.2020.2988055

[13]	PMSM Torque Estimation Based on Machine Learning Techniques 2020 International Conference on Power, Energy and Innovations (ICPEI), 2020 DOI:10.1109/ICPEI49860.2020.9431433

[14]	Nonlinear Autoregressive Neural Network Models for Sea Level Prediction, Study Case: in Semarang, Indonesia 2019 7th International Conference on Information and Communication Technology (ICoICT), 2019 DOI:10.1109/ICoICT.2019.8835307

[15]	Does time series analysis confirms the relationship between space weather effects and the failures of electrical grids in South Poland? Journal of Mathematics in Industry, 2019 DOI:10.1186/s13362-019-0064-9

[16]	Improving the diagnosis of incipient faults in power transformers using dissolved gas analysis and multi layer perceptron 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), 2019 DOI:10.1109/ISIE.2019.8781242

[17]	Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations Energies, 2018 DOI:10.3390/en11071691

[18]	Prediction of the Dst Index and Analysis of Its Dependence on Solar Wind Parameters Using Neural Network Space Weather, 2018 DOI:10.1029/2018SW001863

[19]	Designing Weather Forecasting Model Using Computational Intelligence Tools Applied Artificial Intelligence, 2018 DOI:10.1080/08839514.2018.1530858

[20]	Prediction of the Dst Index and Analysis of Its Dependence on Solar Wind Parameters Using Neural Network Space Weather, 2018 DOI:10.1029/2018SW001863

[21]	Data‐driven approach for fault detection and isolation in nonlinear system International Journal of Adaptive Control and Signal Processing, 2018 DOI:10.1002/acs.2931

[22]	Prediction of hot metal silicon content in blast furnace based on EMD and DNN 2015 34th Chinese Control Conference (CCC), 2015 DOI:10.1109/ChiCC.2015.7260943

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