A New Approach for the 10.7-cm Solar Radio Flux Forecasting: Based on Empirical Mode Decomposition and LSTM

Authors

Junqi Luo^{1, 2, 3,} , Liucun Zhu^{1, *}, Hongbing Zhu¹, Wei Chien³, Jiahai Liang³

¹Advanced Science and Technology Research Institute, Beibu Gulf University, Qinzhou, 535000, Guangxi, China

²College of Mechanical Engineering, Guangxi University, Nanning, 530004, Guangxi, China

³College of Electric and Information Engineering, Beibu Gulf University, Qinzhou, 535000, Guangxi, China

^*Corresponding author. Email: air380@foxmail.com

DOI

10.2991/ijcis.d.210602.001How to use a DOI?

Keywords

Solar radio flux; Time series forecasting; Empirical mode decomposition (EMD); LSTM

Abstract

The daily 10.7-cm Solar Radio Flux (F10.7) data is a time series with highly volatile. The accurate prediction of F10.7 has a great significance in the fields of aerospace and meteorology. At present, the prediction of F10.7 is mainly carried out by linear models, nonlinear models, or a combination of the two. The combination model is a promising strategy, which attempts to benefit from the strength of both. This paper proposes an Empirical Mode Decomposition (EMD) -Long Short-Term Memory Neural Network (LSTMNN) hybrid method, which is assembled by a particular frame, namely EMD–LSTM. The original dataset of F10.7 is firstly processed by EMD and decomposed into a series of components with different frequency characteristics. Then the output values of EMD are respectively fed to a developed LSTM model to acquire the predicted values of each component. The final forecasting values are obtained after a procedure of information reconstruction. The evaluation is undertaken by some statistical evaluation indexes in the cases of 1-27 days ahead and different years. Experimental results show that the proposed method gives superior accuracy as compared with benchmark models, including other isolated algorithms and hybrid methods.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Junqi Luo
AU  - Liucun Zhu
AU  - Hongbing Zhu
AU  - Wei Chien
AU  - Jiahai Liang
PY  - 2021
DA  - 2021/06/11
TI  - A New Approach for the 10.7-cm Solar Radio Flux Forecasting: Based on Empirical Mode Decomposition and LSTM
JO  - International Journal of Computational Intelligence Systems
SP  - 1742
EP  - 1752
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210602.001
DO  - 10.2991/ijcis.d.210602.001
ID  - Luo2021
ER  -