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The global impact of space weather has created interest in understanding and prediction of geomagnetic storms. Most approaches have considered the prediction of geomagnetic storms based on the time series. In this study, two characteristics of geomagnetic storms will be considered – the time interval and difference in value between two consecutive storms. The Kp index was used as a proxy for geomagnetic activities with values greater than or equal to 4 considered as a storm. The Transformer Convolution Network (TCN) deep learning algorithm was used for the prediction. Optimal model parameters for the two indices were obtained using hyperparameter tuning. Results obtained showed that TCN has good performance in predicting the difference in value but limited performance in time intervals. The limitations of TCN in predicting the inter-storm interval can be attributed to its chaotic nature.

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Deep Learning Prediction of Inter-storm Parameters Using Transformer Convolution Network

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Samuel Ogunjo,
  • Babatunde Rabiu,
  • Ibiyinka Fuwape,
  • Oluwatoyin Atikekeresola

摘要

The global impact of space weather has created interest in understanding and prediction of geomagnetic storms. Most approaches have considered the prediction of geomagnetic storms based on the time series. In this study, two characteristics of geomagnetic storms will be considered – the time interval and difference in value between two consecutive storms. The Kp index was used as a proxy for geomagnetic activities with values greater than or equal to 4 considered as a storm. The Transformer Convolution Network (TCN) deep learning algorithm was used for the prediction. Optimal model parameters for the two indices were obtained using hyperparameter tuning. Results obtained showed that TCN has good performance in predicting the difference in value but limited performance in time intervals. The limitations of TCN in predicting the inter-storm interval can be attributed to its chaotic nature.