Time Series Forecasting Model of the Ionospheric f_o F2 Using Facebook Prophet

Authors

  • S. A. Bello Department of Physics, Faculty of Physical Sciences, University of Ilorin, Nigeria.
  • N. S. Hamid Space Science Centre (ANGKASA), Institute of Climate Changes, Universiti, Kebangsaan Malaysia, Malaysia. & Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Malaysia.
  • M. Abdullah Space Science Centre (ANGKASA), Institute of Climate Changes, Universiti, Kebangsaan Malaysia, Malaysia. & Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built, Environment, Universiti Kebangsaan Malaysia, Malaysia.
  • M. A. Ameen Pakistan Space & Upper Atmosphere Research Commission (SUPARCO), Pakistan. & Institute of Space Science & Technology (ISST), University of Karachi, Pakistan.
  • J. S. Shehu Department of Physics, Faculty of Physical Sciences, Bayero University Kano, Nigeria.
  • S. B. Sharafa Department of Physics, Faculty of Physical Sciences, University of Ilorin, Nigeria.
  • F. O. Aweda Physics Programme, College of Agriculture Engineering and Sciences, Bowen University, Nigeria.
  • K. A. Yusuf Department of Physics, Faculty of Physical Sciences, University of Ilorin, Nigeria.
  • S. Yahaya Department of Physics, Faculty of Physical Sciences, University of Ilorin, Nigeria.

DOI:

https://doi.org/10.63746/njtd.v23i1.4395

Keywords:

Ionosphere, Critical frequency, Facebook Prophet, f_o F2, Time series forecasting

Abstract

The study of ionospheric behaviour is critical for maintaining the reliability of space-based technologies, including HF communication links and Global Navigation Satellite Systems (GNSS). Ionospheric delays can significantly degrade the performance and accuracy of trans-ionospheric signals; thus, robust forecasting of the parameter is essential. This study uses a long-term dataset (2009 to 2016 for training) of observations from the Jicamarca Observatory in Peru to implement and evaluate the Facebook Prophet (FB-Prophet) time-series forecasting technique. For the years 2017 to 2019, the FB-Prophet model was used to forecast. Its performance was verified using independent observational test data and accepted empirical standards, namely the IRI-2020 URSI and CCIR sub-options. The study results indicate that FB-Prophet effectively reproduces the diurnal and semi-annual variations in the ionosphere. During the initial forecast period (2017 to 2018), FB-Prophet demonstrated superior alignment with observational fluctuations compared to empirical models, achieving a Root Mean Square Error (RMSE) of 0.71 MHz and a Mean Absolute Error (MAE) of 0.57 MHz. However, as the forecast horizon extended into the deep solar minimum of 2019, the CCIR model exhibited higher precision with a minimum RMSE of 0.15 MHz, while FB-Prophet's uncertainty interval expanded as the lead time increased and the solar environment transitioned into a significantly quieter regime. These findings demonstrate that FB-Prophet’s time-series decomposition is a highly effective tool for multi-year ionospheric forecasting at equatorial latitudes. The study highlights that while empirical models remain reliable for long-term climatological averages, the FB-Prophet approach offers enhanced predictive accuracy for capturing non-linear variations during periods of moderate solar activity (107.68 ± 33.81 sfu).

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Published

2026-03-31

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