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Denoising Nonlinear Time Series Using Singular Spectrum Analysis and Fuzzy Entropy |
| Jian Jiang1, Hong-Bo Xie2** |
1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094
2ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane 4000, Australia
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| Cite this article: |
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Jian Jiang, Hong-Bo Xie 2016 Chin. Phys. Lett. 33 100501 |
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Abstract 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–Glass attractors, as well as improving the multi-step prediction quality of these two series in noisy environments.
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Received: 09 June 2016
Published: 27 October 2016
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| PACS: |
05.45.Tp
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(Time series analysis)
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05.45.Ac
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(Low-dimensional chaos)
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95.75.Wx
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(Time series analysis, time variability)
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