Gap-filling by the empirical mode decomposition
Gap-filling by the empirical mode decomposition
复制标题
通过经验模态分解填补空白
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
P. Flandrin
中科院分区：
文献类型：
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作者：
Azadeh Moghtaderi;P. Borgnat;P. Flandrin
我们提出了一种基于经验模态分解（EMD）的空白填充技术。其思想是，具有缺失数据的信号可以分解为具有缺失数据的一组固有模态函数（imf）。填补每个IMF信号的空白应该比填补原始信号的空白更容易。这是因为每个IMF的变化比原始信号慢得多，也因为IMF已知具有有用的规律性。我们展示了我们的技术在环境污染物数据上的性能。
We propose a novel gap-filling technique, based on the empirical mode decomposition (EMD). The idea is that a signal with missing data can be decomposed into a set of intrinsic mode functions (IMFs) with missing data. Filling the gaps in each IMF should be easier than filling the gaps in the original signal. This is because each IMF varies much more slowly than the original signal, and also because the IMFs are known to have useful regularity properties. We demonstrate the performance of our technique on environmental pollutant data.