과제정보
This work is supported by the Science and Technology Research Program of the Chongqing Municipal Education Commission of China (Grant No. KJQN201901306, KJQN201901350, KJQN201801325).
참고문헌
- Abdel-Rahman, H. I., & Marzouk, B. A. 2018, Statistical Method to Predict the Sunspots Number, NRIAG-JAG, 7, 175
- Akhavan-Amjadi, M. 2020, Fetal Electrocardiogram Modeling Using Hybrid Evolutionary Firefly Algorithm and Extreme Learning Machine, MSSP, 31, 117
- Alejandro, M. R., Smith, K. L., & Roberts, P. C. E. 2018, Solar Activity Simulation and Forecast with a Fluxtransport Dynamo, MNRAS, 479, 3791 https://doi.org/10.1093/mnras/sty1625
- Andreeva, O.A., Abramenko, V.I. & Malaschuk, V.M. 2020, Coronal Holes during the Period of Maximum Asymmetry in the 24th Solar Activity Cycle, Astrophys., 63, 114 https://doi.org/10.1007/s10511-020-09619-2
- Bhowmik, P., & Nandy, D. 2018, Prediction of the Strength and Timing of Sunspot Cycle 25 Reveal Decadal-scale Space Environmental Conditions, Nat. Commun., 9, 1 https://doi.org/10.1038/s41467-017-02088-w
- Bruevich, E. A., & Bruevich, V. V. 2018, Powerful Solar Flares in September 2017. Comparison with the Largest Flares in Cycle 24, Astrophys., 61, 241 https://doi.org/10.1007/s10511-018-9531-z
- Chae, J., & Kim, Y. H. 2017, Performance of the Autoregressive Method in Long-term Prediction of Sunspot Number, JKAS, 50, 21
- Chang, H. Y. 2015, Latitudinal Distribution of Sunspots and Duration of Solar Cycles, JKAS, 48, 325
- Cho, I. H., Bong, S. C., Cho, K. S., et al. 2014, Statistical Study on Personal Reduction Coefficients of Sunspot Numbers since 1981, JKAS, 47, 255
- Covas, E., Peixinho, N., & Fernandes, J. 2019, Neural Network Forecast of the Sunspot Butterfly Diagram, Sol. Phys., 294, 24 https://doi.org/10.1007/s11207-019-1412-z
- Dean, P. W.,& Schatten, K. H.. 2018, An Early Prediction of the Amplitude of Solar Cycle 25, Sol. Phys., 293, 112 https://doi.org/10.1007/s11207-018-1330-5
- Deng, L. H., Xiang, Y. Y., Qu, Z. N., & An, J. M. 2016, Systematic Regularity of Hemispheric Sunspot Areas over the Past 140 Years, AJ, 151, 70 https://doi.org/10.3847/0004-6256/151/3/70
- Deng H., Mei Y., & Wang F. 2020, Periodic Variation and Phase Analysis of Grouped Solar Flare with Sunspot Activity, RAA, 20, 022
- Ding, L. G., Lan, R. S., Jiang, Y., & Peng, J. D. 2012, Prediction of the Smoothed Monthly Mean Sunspot Area Based on Neural Network, Trans. Atmosph. Sci., 35, 508
- Donskikh, G. I., Ryabov, M. I., Sukharev, A. L., & Aller, M. 2016, Singular-spectrum Analysis and Wavelet Analysis of the Variability of the Extragalactic Radio Sources 3C 120 and CTA 102, Astrophys., 59, 199 https://doi.org/10.1007/s10511-016-9427-8
- Florios, K., Kontogiannis, I., Park, S. H., et al. 2018, Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning, Sol. Phys., 293, 28 https://doi.org/10.1007/s11207-018-1250-4
- Gong, B. M., Wang, W. B., & Zhao, P. 2014, EMD-FSVM Prediction for Nonstationary Time Series, Computer Sci., 041, 57
- Gopasyuk, O. S. 2017, Magnetic Field of Sunspots during the Rising Phase of Activity Cycle 24, Astrophys., 60, 90 https://doi.org/10.1007/s10511-017-9465-x
- Guo, Y., Pariat, E., Valori, G., et al. 2017, Magnetic Helicity Estimations in Models and Observations of the Solar Magnetic Field. III. Twist Number Method, ApJ, 840, 40 https://doi.org/10.3847/1538-4357/aa6aa8
- Hamid, R. H., & Marzouk, B. A. 2018, Forecasting the Peak of the Present Solar Activity Cycle 24, NRIAG-JAG, 7, 15
- Hathaway, D. H. 2015, The Solar Cycle, Living Rev. Sol. Phys., 12, 4 https://doi.org/10.1007/lrsp-2015-4
- Huang, N. E., Shen, Z., Long, S. R., et al. 1998, The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis, Proc. Roy. Soc. London A, 454, 903 https://doi.org/10.1098/rspa.1998.0193
- Huang, G. B., Zhou, H., Ding, X., & Zhang, R. 2012, Extreme Learning Machine for Regression and Multiclass Classification, IEEE Transactions on Systems Man & Cybernetics Part B, 42, 513 https://doi.org/10.1109/TSMCB.2011.2168604
- Inceoglu, F., Simoniello, R., Arlt, R., & Rempel, M. 2019, Constraining Non-linear Dynamo Models Using Quasibiennial Oscillations from Sunspot Area Data, A&A, 625, A117 https://doi.org/10.1051/0004-6361/201935272
- Jeong, E. J., Lee, J. Y., Moon, Y. J., & Park, J. 2014, Forecast of Solar Proton Events with NOAA Scales based on Solar X-ray Flare Data Using Neural Network, JKAS, 47, 209
- Kuzanyan, K. M., Safiullin, N., Kleeorin, N., et al. 2019, Large-Scale Properties of the Tilt of Sunspot Groups and Joy's Law Near the Solar Equator, Astrophys. 62, 261 https://doi.org/10.1007/s10511-019-09579-2
- Labonville, F., Charbonneau, P., & Lemerle, A. 2019, A Dynamo-based Forecast of Solar Cycle 25, Sol. Phys., 294, 82 https://doi.org/10.1007/s11207-019-1480-0
- Lee, J. Y., Moon, Y. J., Kim, K. S., et al. 2007, Prediction of Daily Maximum X-ray Flux Using Multilinear Regression and Autoregressive Time-series Methods, JKAS, 40, 99
- Li, F. F. , Wang, S. Y., & Wei, J. H. 2018, Long Term Rolling Prediction Model for Solar Radiation Combining Empirical Mode Decomposition (EMD) and Artificial Neural Network (ANN) techniques, JRSE, 10, 013704
- Li, G., & Wang, S. 2017, Sunspot Time-series Prediction Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Neural Network, MPE, 2017, 1
- Liu, H., Liu, C., Wang, J. T., & Wang, H. 2019, Predicting Solar Flares Using a Long Short-term Memory Network, ApJ, 877, 121 https://doi.org/10.3847/1538-4357/ab1b3c
- Macario-Rojas, A., Smith, K. L., & Roberts, P. C. 2018, Solar Activity Simulation and fForecast with a Fluxtransport Dynamo, MNRAS, 479, 3791 https://doi.org/10.1093/mnras/sty1625
- Miao, J., Wang, X., Ren, T. L., & Li, Z. T. 2020, Prediction Verification of Solar Cycles 18-24 and a Preliminary Prediction of the Maximum Amplitude of Solar Cycle 25 Based on the Precursor Method, RAA, 20, 4
- Miller, T. H., Gallidabino, M. D., Macrae, J. R., et al. 2019, Prediction of Bioconcentration Factors in Fish and Invertebrates Using Machine Learning, Sci. Total Environ., 648, 80 https://doi.org/10.1016/j.scitotenv.2018.08.122
- Pala, Z., & Atici, R. 2019, Forecasting Sunspot Time Series Using Deep Learning Methods, Sol. Phys., 294, 50 https://doi.org/10.1007/s11207-019-1434-6
- Pesnell, W. D.,& Schatten, K. H. 2018, An Early Prediction of the Amplitude of Solar Cycle 25, Sol. Phys., 293, 112 https://doi.org/10.1007/s11207-018-1330-5
- Ravindra, B., & Javaraiah, J. 2015, Hemispheric Asymmetry of Sunspot Area in Solar Cycle 23 and Rising Phase of solar Cycle 24: Comparison of Three Data Sets, New Astron., 39, 55 https://doi.org/10.1016/j.newast.2015.03.004
- Saadi, S., & Chaker, A. 2018, A Numerical Simulation Approach for Sunspot Area Calculation, ETASR, 8, 3013
- Sarp, V., Kilcik, A., Yurchyshyn, V., et al. 2018, Prediction of Solar Cycle 25: A Non-linear Approach, MNRAS, 481, 2981 https://doi.org/10.1093/mnras/sty2470
- Stenflo, J. O. 2017, History of Solar Magnetic Fields since George Ellery Hale, SSR, 210, 5
- Sukharev, A. L., Ryabov, M. I. & Donskikh, G. I. 2016, Predicting Changes in the Radio Emission Fluxes of Extragalactic Sources, Astrophys., 59, 213 https://doi.org/10.1007/s10511-016-9428-7
- Tang, J. 2015, Application of the Grey Topological Theory in the Prediction of Yearly Mean Sunspot Numbers, ChA&A, 39, 45
- Tang, L., Dai, W., Yu, L., & Wang, S. 2015, A Novel CEEMD-based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting, IJITDM, 14, 141
- Tang, L., Wu, Y., & Yu, L. 2018, A randomized-algorithmbased Decomposition Ensemble Learning Methodology for Energy Price Forecasting, Energy, 157,526 https://doi.org/10.1016/j.energy.2018.05.146
- Vorobeva, E. 2019, Notes on the Correlation between Sudden Stratospheric Warmings and Solar Activity, ANGEO, 37, 375
- Wang, H., & Li, G. 2019, Extreme Learning Machine Cox Model for High-dimensional Survival Analysis, Statist. Medicine, 38, 2139 https://doi.org/10.1002/sim.8090
- Warren, H. P., Emmert, J. T., & Crump, N. A. 2017, Linear Forecasting of the F 10.7 Proxy for Solar Activity, Space Weather, 15, 1039 https://doi.org/10.1002/2017SW001637
- Wilson, R. M. 2016, Sunspot Area in Relation to the Newly Revised Sunspot Number, JAAS, 87, 146
- Wu, Z., & Huang, N. E. 2009, Ensemble Empirical Mode Decomposition: A Noise-assisted Data Analysis Method, AADA, 1, 1
- Yadav, H. K., Pal, Y., & Tripathi, M. M. 2020, Short-term PV Power Forecasting Using Empirical Mode Decomposition in Integration with Back-propagation Neural Network, JIOS, 41, 25
- Yeh, J. R., Shieh, J. S., & Huang, N. E. 2010, Complementary Ensemble Empirical Mode Decomposition: A Novel Noise Enhanced Data Analysis Method, AADA, 2, 135