Email this article 
Forecasting Solar Activities based on Sunspot Number Using Support Vector Regression (SVR)
Abstract
Progress on the 4.0 industrial revolution was the most significant is the entire computer and robot is connected to the internet connection. Satellites as one of the internet network transmitters have the threat of destruction if a solar storm occurs. The size of the activity that is on the sun can be known by observing sunspots. Solar activity in the future is known by forecasting sunspot numbers. This research will forecast sunspot numbers using support vector regression (SVR), its aimed to minimized adverse effects on the earth as an outcome of solar storms. The best SVR results on forecast sunspot numbers are on annual sunspot number obtained using RBF kernel. Measurement results from MSE, RMSE, and MAAPE respectively by 35.32, 5.94, and 0.12. Forecasting concluded accurately based on MAAPE value, on 2020 and 2021 indicated potentially flare because the result of forecasting sunspot numbers is more than twenty.
Keywords
Full Text:
PDFReferences
M. A. K. Bahrin et al., "Industry 4.0: A Review on Industrial Automation and Robotic," Jurnal Teknologi, vol. 78, no. 6–13, pp. 137–143, 2016.
A. Botta and A. Pescape, "New Generation Satellite Broadband Internet Services: Should ADSL and 3G Worry?," in The 5th IEEE International Traffic Monitoring and Analysis Workshop (TMA 2013), Turin: Universita Degli Studi di Milano, 2013.
G. Chattopadhyay and S. Chattopadhyay, "Monthly Sunspot Number Time Series Analysis and Its Modeling Through Autoregressive Artificial Neural Network," The Europ. Phys. J. Plus, vol. 127, no. 43, pp. 1–8, 2012.
A. Gkana and L. Zachilas, "Sunspot Numbers: Data Analysis, Predictions, and Economic Impacts," J. Eng. Sci. Technol. Rev (JESTR), vol. 8, no. 1, pp. 79–85, 2015.
Zulkarnain and A. Putriani, "Korelasi Bilangan Sunspot sebagai Parameter Produktivitas Flare pada Siklus Matahari Ke-24," in Pros. Sem. Nas. Fis., Riau: Universitas Riau, 2017.
D. H. Hathaway, "The Solar Cycle," Living Rev. in Solar Phys., vol. 12, no. 4, pp. 12–13, 2015.
P. S. McIntosh, "The Classification of Sunspot Groups," Solar Phys., vol. 125, no.2, pp. 251–267, 1990.
D. C. R. Novitasari, N. Ardhiyah, and N. Widodo, "Flare Identification by Forecasting Sunspot Number using Fuzzy Time Series Markov Chain Model," in Int. Sem. Intell. Technol. and Its Appl. (ISITIA), Surabaya: Institut Teknologi Sepuluh Nopember Surabaya, 2019.
T. Xu et al., "Long-Term Sunspot Number Prediction based on EMD Analysis and AR Model," Chinese J. Astron. Astrophys., vol. 8, no. 3, pp. 337–342, 2008.
G. Li, X. Ma, and H. Yang, "A Hybrid Model for Forecasting Sunspots Time Series based on Variational Mode Decomposition and Backpropagation Neural Network Improved by Firefly Algorithm," Hindawi Computat. Intell. Neurosci., vol. 2018, pp. 1–9, 2018.
D. C. R. Novitasari et al., "Weather Parameters Forecasting as Variables for Rainfall Prediction using Adaptive Neuro Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR)," J. Phys.: Conf. Ser., vol. 1501, p. 012012, 2020.
S. D. Agustina et al., "Support Vector Regression Algorithm Modeling to Predict the Availability of Foodstuff in Indonesia to Face the Demographic Bonus," J. Phys.: Conf. Ser., vol. 1028, p. 012240, 2018.
E. M. Priliani, A. T. Putra, and M. A. Muslim, "Forecasting Inflation Rate using Support Vector Regression (SVR) based Weight Attribute Particle Swarm Optimization (WAPSO)," Scient. J. Inform., vol. 5, no.2, pp. 118–127, 2018.
A. Fanani et al., "Regression Model Focused on Query for Multi Documents Summarization based on Significance of the Sentence Position," TELKOMNIKA, vol. 17, no. 6, pp. 3050–3056, 2019.
S. R. Binaiya, M. N. Hayati, and I. Purnamasari, "Peramalan Menggunakan Time Invariant Fuzzy Time Series (Studi Kasus: Indeks Harga Konsumen Provinsi Kalimantan Timur)," EKSPONENSIAL,vol. 10, no. 2, pp. 175–182, 2019.
N. Rukhansah, M. A. Muslim, and R. Arifudin, "Fuzzy Time Series Markov Chain dalam Meramalkan Harga Saham," in Sem. Nas. Ilm. Komp. (SNIK), Surakarta: Universitas Sebelas Maret, 2015.
D. A. Adyanti et al., "Forecasts Marine Weather on Java Sea using Hybrid Methods: TS-ANFIS," in Proc. 4th Int. Conf. Electric. Eng. Comp. Sci. Inform. (EECSI), Yogyakarta: Institute of Advanced Engineering and Science, 2017.
A. Mehedintu, M. Sterpu, and G. Soava, "Estimation and Forecasts for the Share of Renewable Energy Consumption in Final Energy Consumption by 2020 in the European Union," Sustainability, vol. 10, no. 5, pp. 1–22, 2018.
T. Wang, "Forecast of Economic Growth by Time Series and Scenario Planning Method: A Case Study of Shenzhen," Scient. Res. Publ., vol. 7, no. 2, pp. 212–222, 2016.
B. Santosa, Tutorial Support Vector Machines, Surabaya: Institut Teknologi Sepuluh Nopember Surabaya, 2015.
A. J. Smola and B. Scholkopf, "A Tutorial on Support Vector Regression," Stat. Comp., vol. 14, no. 3, pp. 199–222, 2004.
S. Saigal and D. Mehrotra, "Performance Comparison of Time Series Data using Predictive Data Mining Techniques," Adv. Inform. Min., vol. 4, no. 1, pp. 57–66, 2012.
S. Kim and H. Kim, "A New Metric of Absolute Percentage Error for Intermittent Demand Forecasts," Int. J. Forecasting, vol. 32, no. 3, pp. 669–679, 2016.
B. Setiahadi, " Limit Bawah Bilangan Sunspot untuk Sunspot yang Berpotensi Melepaskan Flare: Metode Peringatan Dini Antariksa," in Sem. Nas. Inov. Apl. Teknol. Indus., Malang: Institut Teknologi Nasional, 2017.
Copyright (c) 2020 Suwanto Suwanto, Dian Candra Rini Novitasari
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License