Performance of AICC Statistic as Time Series Modeling and Forecasting of Sunspots
Keywords:
Akaike's Information Corrected Criterion (AICC) Statistic; Sunspots datasets
Abstract
Dynamics of the Sun is quite complex. Sunspot number is an important index to understand the solar dynamo mechanism that governs the solar magnetic cycle. This paper is an attempt to forecast Sunspots number by finding an appropriate time series model. For the purpose, Sunspots datasets from 1749 to 2010 were used. Also, 23 minimum to minimum (m-m) and 24 maximum to minimum (M-m) Sunspots cycles were investigated. The results show that Autoregressive Moving average (ARMA) time series models fit the Sunspots number well. They have corrected predictions of the future trend of the Sunspots number within the sample period of study. The results obtained showed that the probability of the AIC corrected model was found better fitted. The problem of over parameterization exited in the model used, but under parameterization was found to minimal.
References
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[2] Ali, K.; Hassan, D., Murshad, R; Sunspots Activity Cycles Using Linear and Non-Linear Technique. Journal of Information Communication Technologies and Robotic Applications 2018
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[4] Amjad, M.; Khan, A.;Fatima, K.; Ajaz, O.; Ali, S.; Main, Analysis of Temperature Variability, Trends and Prediction in the Karachi Region of Pakistan Using ARIMA Models. Atmosphere 2023, 14, 88.https://doi.org/10.3390/ atmos14010088
[5] Bothmer, V., &Daglis, I. A. (2007). Space weather: physics and effects. Springer Science & Business Media.
[6] Bray, R. J., & Lough head, R. E. (1964). Sunspots. Modelling Global Solar Radiation: Case Study of Ibadan, Nigeria. International Journal of Applied Science and Engineering, 13(3), 233-245 Babcock, H. W. (1962). The Solar Magnetic Cycle, Trans. I. A. U
[7] Babcock, H. W. (1963). The sun's magnetic field. Annual Review of Astronomy a Astrophysics, 41.
[8] Bartels, J. (1963). Discussion of time-variations of geomagnetic activity, indices Kp And Ap, 1932-1961. In Annales de Geophysics (Vol. 19, p. 1
[9] Brockwell P J and Bavis R A (1994), “ITSM for Windows”, Springer, New York.
[10] Hurvich. C. M. And Tsai C.L (1989), Regression and Time Series Model Selection in Small Samples, Biometrika, 76,297-307.
[11] Hastings, H. M., & Sugihara, G. (1993).Fractals. A user's guide for the natural Sciences. Oxford Science Publications, Oxford, New York: Oxford University Press c1993, 1.
[12] Hewish, A. (1988). The solar origin of geomagnetic storms. Solar physics, 116(1), 195-198
[13] Higginson, M. J., Altabet, M. A., Wincze, L., Herbert, T. D., & Murray, D. W. (2004). A solar (irradiance) trigger for millennial‐scale abrupt changes in the Southwest monsoon? Palaeoceanography, 19 (3).
[14] Horn, R. & Johnson, C. (1985). Matrix Analysis, UK, Cambridge: Cambridge University Press, (Chapter 8).MacDonald, I. & Zucchini, W. (1997). Hidden Marko and Other Models for Discrete-valued Time Series. London: Chapman & Hall, (Chapter 5)
[15] Kilcik, A., Anderson, C. N. K., Rozelot, J. P., Ye, H., Sugihara, G., & Ozguc, A. (2009).Nonlinear prediction of solar cycle 24. The Astrophysical Journal, 693(2), 1173
[16] M.Y.Cho, J.C. Hwang and C.S. Chen (1995), Customer Short Term Load Forecasting by Using ARIMA Transfer Function Model. International Conference on Energy Management and Power Delivery.
[17] N. Amjady (2001), Short -Term Hourly Load Estimation Capability, IEEE Transactions on Power Systems.Vol.16
[18] P.J. Brockwell and R. A. Davis, (1996), Introduction to time series and forecasting. Springer texts in statistics, Springer-Verlag, New York, Ch.1-5
[19] Schaffer, et al. Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: A guide for evaluating large-scale health interventions. BMC Medical Research Methodology 2021, 21:58. doi.org/10.1186/s12874-021-01235-8
[20] Schwab H. (1844) Astronomische Nachrichten 21 (495), 254-256.
Published
2023-01-31
How to Cite
Khan, A., Murshid, S., Anwer, A., Ali, A., Afrahim, S., & Zafar, M. (2023). Performance of AICC Statistic as Time Series Modeling and Forecasting of Sunspots. International Journal of Artificial Intelligence & Mathematical Sciences, 1(2), 16-25. https://doi.org/10.58921/ijaims.v1i2.38
