Denoising Nonlinear Time Series Using Singular Spectrum Analysis and Fuzzy Entropy

被引：0

作者：

江剑 ^{[1
]}

谢洪波 ^{[2
]}

机构：

[1] School of Mechanical Engineering,Nanjing University of Science and Technology

[2] ARC Centre of Excellence for Mathematical and Statistical Frontiers,Queensland University of Technology

来源：

Chinese Physics Letters | 2016年 / 10期

关键词：

of; on; or; in; Denoising Nonlinear Time Series Using Singular Spectrum Analysis and Fuzzy Entropy; NLP; is;

D O I：

暂无

中图分类号：

TN911.7 [信号处理]; O211.61 [平稳过程与二阶矩过程];

学科分类号：

020208 ; 070103 ; 0711 ; 0714 ; 080401 ; 080402 ;

摘要：

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-GIass attractors,as well as improving the multi-step prediction quality of these two series in noisy environments.

引用

页码：23 / 27

页数：5

共 50 条

[31] Time-series image denoising of pressure-sensitive paint data by projected multivariate singular spectrum analysis
Yuya Ohmichi
Kohmi Takahashi
Kazuyuki Nakakita
[J]. Experiments in Fluids, 2022, 63
[32] Improvement of Separability of Time Series in Singular Spectrum Analysis Using the Method of Independent Component Analysis
Golyandina, N. E.
Lomtev, M. A.
[J]. VESTNIK ST PETERSBURG UNIVERSITY-MATHEMATICS, 2016, 49 (01) : 9 - 17
[33] Time-series image denoising of pressure-sensitive paint data by projected multivariate singular spectrum analysis
Ohmichi, Yuya
Takahashi, Kohmi
Nakakita, Kazuyuki
[J]. EXPERIMENTS IN FLUIDS, 2022, 63 (11)
[34] Using singular spectrum analysis for inference on seasonal time series with seasonal unit roots
Thomakos, Dimitrios D.
Hassani, Hossein
[J]. INTERNATIONAL JOURNAL OF COMPUTATIONAL ECONOMICS AND ECONOMETRICS, 2020, 10 (02) : 149 - 182
[35] IDENTIFYING SOUTH AFRICAN TOURISM TIME SERIES STRUCTURE USING SINGULAR SPECTRUM ANALYSIS
de Klerk, J.
[J]. SOUTH AFRICAN STATISTICAL JOURNAL, 2014, 48 (01) : 19 - 39
[36] HYDROLOGICAL TIME SERIES INVESTIGATION AND FORECASTING USING "CATERPILLAR" - SINGULAR SPECTRUM ANALYSIS APPROACH
Polukoshko, Svetlana
Hilkevica, Galina
[J]. GEOCONFERENCE ON WATER RESOURCES, FOREST, MARINE AND OCEAN ECOSYSTEMS, VOL I (SGEM 2014), 2014, : 389 - 396
[37] Based on Singular Spectrum Analysis in the Study of GPS Time Series Analysis
Qiu, Ronghai
Cheng, Yingyan
Wang, Hu
Wang, Xiaoming
[J]. CHINA SATELLITE NAVIGATION CONFERENCE (CSNC) 2015 PROCEEDINGS, VOL I, 2015, 340 : 217 - 226
[38] A review on singular spectrum analysis for economic and financial time series
Hassani, Hossein
Thomakos, Dimitrios
[J]. STATISTICS AND ITS INTERFACE, 2010, 3 (03) : 377 - 397
[39] Hybrid quantum singular spectrum decomposition for time series analysis
Postema, J. J.
Bonizzi, P.
Koekoek, G.
Westra, R. L.
Kokkelmans, S. J. J. M. F.
[J]. AVS QUANTUM SCIENCE, 2023, 5 (02):
[40] Singular Spectrum Analysis for time series: Introduction to this special issue
Zhigljavsky, Anatoly
[J]. STATISTICS AND ITS INTERFACE, 2010, 3 (03) : 255 - 258

← 1 2 3 4 5 →