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Data Synthesis Based on Empirical Mode Decomposition
The purpose of this paper is to introduce an effective way to solve the problem of nonstationary data generation. Empirical mode decomposition (EMD) algorithms have been widely used in data diagnosis. A new EMD-based data synthesis method is proposed. The method utilizes the recombination of the intrinsic mode function (IMF) of the segmented data, as well as the characteristics of the residuals, to generate the data. This article takes the 100-year monthly temperature and rainfall data of Tainan, Taiwan, as an example. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test is applied in the paper to verify the stationarity of the generated data. The EMD-based data synthesis effectively shows its applicability and provides new ideas for nonstationary data generation.
Data Synthesis Based on Empirical Mode Decomposition
The purpose of this paper is to introduce an effective way to solve the problem of nonstationary data generation. Empirical mode decomposition (EMD) algorithms have been widely used in data diagnosis. A new EMD-based data synthesis method is proposed. The method utilizes the recombination of the intrinsic mode function (IMF) of the segmented data, as well as the characteristics of the residuals, to generate the data. This article takes the 100-year monthly temperature and rainfall data of Tainan, Taiwan, as an example. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test is applied in the paper to verify the stationarity of the generated data. The EMD-based data synthesis effectively shows its applicability and provides new ideas for nonstationary data generation.
Data Synthesis Based on Empirical Mode Decomposition
Huang, Wen-Cheng (author) / Chu, Tai-Yi (author) / Jhang, Yi-Syuan (author) / Lee, Jyun-Long (author)
2020-04-30
Article (Journal)
Electronic Resource
Unknown
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