Abstract

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.

中文翻译：

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输入特征对于航天器数据域适应的意义

摘要

改进地磁指数Dst的神经网络预测的问题是，在这样的情况下，这种预测的输入数据是由两艘航天器测量的，其中一艘接近其生命周期的终点，而另一艘的数据历史为还不足以构建所需质量的神经网络预测。为了从一个航天器的数据到另一个航天器的数据的有效转换，有必要使用域适应方法。本文测试并比较了几种数据翻译方法。此外，对于每个翻译的属性，找到了其翻译的最佳参数集，这进一步减少了域之间的差异。该论文表明，与使用未翻译数据的结果相比，使用领域适应方法并选择重要特征可以改善预测。