The Significance of Input Features for Domain Adaptation of Spacecraft Data

E. Z. Karimov; Каримов Э. З.; I. N. Myagkova; Мягкова И. Н.; V. R. Shirokiy; Широкий В. Р.; O. G. Barinov; Баринов О. Г.; S. A. Dolenko; Доленко С. А.

doi:10.31857/S0023420623600125

The Significance of Input Features for Domain Adaptation of Spacecraft Data

Autores: Karimov E.Z.¹^,2, Myagkova I.N.², Shirokiy V.R.², Barinov O.G.², Dolenko S.A.³
Afiliações:
1. Faculty of Physics, Moscow State University, 119991, Moscow, Russia
2. Skobeltsyn Institute of Nuclear Physics, Moscow State University, 119991, Moscow, Russia
3. Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University
Edição: Volume 61, Nº 6 (2023)
Páginas: 530-537
Seção: Articles
URL: https://rjmseer.com/0023-4206/article/view/672588
DOI: https://doi.org/10.31857/S0023420623600125
EDN: https://elibrary.ru/CBGXUK
ID: 672588

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Resumo

The problem of improving the neural network forecast of geomagnetic index Dst under conditions in which the input data for such a forecast are measured by two spacecraft, one of which is close to the end of its life cycle, and the data history of the other is not yet enough to construct a neural network forecast of the required quality. For an efficient transition from the data of one spacecraft to the data of another, it is necessary to use methods of domain adaptation. This paper tests and compares several data translation methods. Also, for each translated attribute, an optimal set of parameters for its translation were found, which further reduces the difference between domains. The paper shows that the use of domain adaptation methods with the selection of significant features can improve the forecast compared to the results of using untranslated data.

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