Denoising Nonlinear Time Series Using Singular Spectrum Analysis and Fuzzy Entropy

We present a hybrid singular spectrum analysis （SSA） and fuzzy entropy method to filter noisy nonlinear time series. With this approach, SSA decomposes the noisy time series into its constituent components including both the deterministic behavior and noise, while fuzzy entropy automatically differe...

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Bibliographic Details
Published inChinese physics letters Vol. 33; no. 10; pp. 19 - 23
Main Author 江剑 谢洪波
Format Journal Article
LanguageEnglish
Published Chinese Physical Society and IOP Publishing 01.10.2016
Subjects
去噪
噪声环境
复杂噪声
多步预测
奇异谱分析
模糊熵
混合方法
非线性时间序列
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ISSN0256-307X
1741-3540
DOI10.1088/0256-307X/33/10/100501

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Summary:We present a hybrid singular spectrum analysis （SSA） and fuzzy entropy method to filter noisy nonlinear time series. With this approach, SSA decomposes the noisy time series into its constituent components including both the deterministic behavior and noise, while fuzzy entropy automatically differentiates the optimal dominant components from the noise based on the complexity of each component. We demonstrate the effectiveness of the hybrid approach in reconstructing the Lorenz and Mackey--Class attractors, as well as improving the multi-step prediction quality of these two series in noisy environments.
Bibliography:11-1959/O4
We present a hybrid singular spectrum analysis （SSA） and fuzzy entropy method to filter noisy nonlinear time series. With this approach, SSA decomposes the noisy time series into its constituent components including both the deterministic behavior and noise, while fuzzy entropy automatically differentiates the optimal dominant components from the noise based on the complexity of each component. We demonstrate the effectiveness of the hybrid approach in reconstructing the Lorenz and Mackey--Class attractors, as well as improving the multi-step prediction quality of these two series in noisy environments.
ISSN:0256-307X
1741-3540
DOI:10.1088/0256-307X/33/10/100501