Modified Seasonal Decomposition Variations of Earth Magnetic Field Induction Module

S. A. Imashev, S. V. Parov
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Abstract

In this paper, we present a modification of the classic method of seasonal decomposition of the time series, in particular its application for the analysis of geomagnetic data. Seasonal decomposition is a powerful tool for time series analysis, but its classic implementation does not always provide accurate results when the time series contains amplitude outliers and prolonged gaps. We propose a modified approach to solve this task of seasonal decomposition, by applying an average daily profile. This ensures the extraction of various anomalies in the residual component of the decomposition, in particular, global and contextual outliers, as well as disturbances due to magnetic storms in the variations of geomagnetic field induction module. Keywords: geomagnetic field, seasonal decomposition, data gaps, autocorrelation function, residual component, outliers, magnetic storm, DST index
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地球磁场感应模块的修正季节分解变化
在本文中,我们介绍了对时间序列季节分解这一经典方法的修改,特别是将其应用于地磁数据分析。季节分解是时间序列分析的有力工具,但当时间序列包含振幅离群值和长期间隙时,其经典实施方法并不总能提供准确的结果。我们提出了一种改进的方法,通过应用日平均剖面来解决季节分解问题。这可确保提取分解残余部分中的各种异常情况,特别是全局和上下文异常值,以及地磁场感应模块变化中的磁暴干扰。关键词:地磁场;季节分解;数据间隙;自相关函数;残差分量;异常值;磁暴;DST 指数
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