Eur. Phys. J. Plus (2022) 137: 479
https://doi.org/10.1140/epjp/s13360-022-02687-7

Regular Article

One-hour ahead prediction of the Dst index based on the optimum state space reconstruction and pattern recognition

¹ Department of Physics, Indian Institute of Engineering Science and Technology, Shibpur, P.O.-Botanic Garden, 711103, Howrah, West Bengal, India
² Department of Electronics, Dinabandhu Andrews College, 54, Raja S.C. Mullick Road, Garia, 700084, Kolkata, West Bengal, India

^b adrija_bnrj@yahoo.co.in

Received: 5 July 2021
Accepted: 5 April 2022
Published online: 18 April 2022

Abstract

The Disturbance storm time (Dst) index is the global estimation of the intensity of the terrestrial geomagnetic activities as well as the primary indicator of the geomagnetic storm. As the adverse effect of a powerful geomagnetic event has become a serious threat to modern human society, extracting meaningful information concealed in the complex structures of this time series can form the ground of a successful prediction algorithm. In this paper, we introduce a new probabilistic model based on the concept of adaptive delta modulation and optimum state space to analyze, identify and characterize the patterns enfolded in the layers of the 1-h Dst index and then predict Dst data using these patterns. The study reveals some significant insights. The exact dimension M of the optimum state space for the Dst index is found to be at M = 10. Also, the series is a combination of multiple distinguished repeating and non-repeating patterns, denoting a high degree of predictability of the Mth data of the series from its previous (M-1) binary data. Eventually, the simulated output of the probabilistic model exhibits a high value of correlation coefficient with the real-time Dst index. Interestingly, the technique only requires the Dst index data as an input of the probabilistic model and is found to be very effective for the entire 24th solar cycle.