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...
Saved in:
| Published in | Chinese physics letters Vol. 33; no. 10; pp. 19 - 23 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Chinese Physical Society and IOP Publishing
01.10.2016
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0256-307X 1741-3540 |
| DOI | 10.1088/0256-307X/33/10/100501 |
Cover
| 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--Class attractors, as well as improving the multi-step prediction quality of these two series in noisy environments. |
|---|---|
| AbstractList | 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. 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. |
| Author | 江剑 谢洪波 |
| AuthorAffiliation | School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094 ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbmle 4000, Australia |
| Author_xml | – sequence: 1 fullname: 江剑 谢洪波 |
| BookMark | eNqFkN9rwjAQx8NwMHX7F0bYe-eladoU9iJOt4FsMBX2VtKaukhNuqQ-1L9-6RQf9iIcd3B3n_vxHaCeNloidE_gkQDnIwhZHFBIvkaUjgh4AwbkCvVJEpGAsgh6qH9uukED57YAhHBC-ujzWWqjnNIb_G50pbQUFi_VTuKFtEo6vPqrLbzbV760qGXR2P0Oj7WoWqccFnqNZ_vDocVT3VhTt7fouhSVk3enOESr2XQ5eQ3mHy9vk_E8KGjImoCXkpdQ5CLJZcxTTsJcspDma8hjyCEWJGX-RFgXnEUyZyKNGJFUpgnjVCSUDtHTcW5hjXNWllmhGtEo488QqsoIZJ0-Wfd61r2eUXpMdvp4PP6H11bthG0vg-ERVKbOtmZvvRLuMvRw2vZt9ObHy3leFydAaRhTQn8BbEqF0g |
| CitedBy_id | crossref_primary_10_3390_technologies6010019 crossref_primary_10_1109_TIM_2019_2945826 crossref_primary_10_3233_JCM_170756 |
| Cites_doi | 10.1016/j.physleta.2007.01.027 10.1098/rspa.2014.0409 10.1088/0256-307X/22/11/014 10.1142/S021812749800036X 10.1007/s10439-010-9933-5 10.1103/PhysRevLett.59.845 10.1016/j.asoc.2010.11.020 10.7498/aps.53.3303 10.1016/0167-2789(92)90103-T 10.1007/s12209-015-2461-5 10.1063/1.4812287 10.1016/0167-2789(89)90074-2 10.1016/j.jfranklin.2015.10.015 |
| ContentType | Journal Article |
| Copyright | 2016 Chinese Physical Society and IOP Publishing Ltd |
| Copyright_xml | – notice: 2016 Chinese Physical Society and IOP Publishing Ltd |
| DBID | 2RA 92L CQIGP ~WA AAYXX CITATION |
| DOI | 10.1088/0256-307X/33/10/100501 |
| DatabaseName | 维普期刊资源整合服务平台 中文科技期刊数据库-CALIS站点 维普中文期刊数据库 中文科技期刊数据库- 镜像站点 CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Physics |
| DocumentTitleAlternate | Denoising Nonlinear Time Series Using Singular Spectrum Analysis and Fuzzy Entropy |
| EISSN | 1741-3540 |
| EndPage | 23 |
| ExternalDocumentID | 10_1088_0256_307X_33_10_100501 cpl_33_10_100501 670332631 |
| GroupedDBID | 02O 042 1JI 1PV 1WK 29B 2RA 4.4 5B3 5GY 5VR 5VS 5ZH 7.M 7.Q 92L AAGCD AAJIO AAJKP AALHV AATNI ABHWH ABJNI ABQJV ACAFW ACGFS ACHIP AEFHF AENEX AFUIB AFYNE AHSEE AKPSB ALMA_UNASSIGNED_HOLDINGS ASPBG ATQHT AVWKF AZFZN BBWZM CEBXE CJUJL CQIGP CRLBU CS3 EBS EDWGO EJD EMSAF EPQRW EQZZN FEDTE HAK HVGLF IHE IJHAN IOP IZVLO JCGBZ KNG KOT LAP M45 N5L N9A NS0 NT- NT. P2P PJBAE Q02 R4D RIN RNS RO9 ROL RPA RW3 S3P SY9 T37 UCJ W28 XPP ~02 ~WA -SA -S~ AAXDM AOAED CAJEA Q-- U1G U5K AAYXX ACARI ADEQX AERVB AGQPQ ARNYC CITATION TGP |
| ID | FETCH-LOGICAL-c325t-8fe8f0cba7be689812be523bd0b60b06a1958110dc854eb5a9451e3e97583a733 |
| IEDL.DBID | IOP |
| ISSN | 0256-307X |
| IngestDate | Thu Apr 24 22:55:42 EDT 2025 Tue Jul 01 01:35:30 EDT 2025 Wed Aug 21 03:33:15 EDT 2024 Wed Feb 14 10:10:13 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 10 |
| Language | English |
| License | http://iopscience.iop.org/info/page/text-and-data-mining http://iopscience.iop.org/page/copyright |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c325t-8fe8f0cba7be689812be523bd0b60b06a1958110dc854eb5a9451e3e97583a733 |
| Notes | 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. |
| PageCount | 5 |
| ParticipantIDs | chongqing_primary_670332631 iop_journals_10_1088_0256_307X_33_10_100501 crossref_citationtrail_10_1088_0256_307X_33_10_100501 crossref_primary_10_1088_0256_307X_33_10_100501 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2016-10-01 |
| PublicationDateYYYYMMDD | 2016-10-01 |
| PublicationDate_xml | – month: 10 year: 2016 text: 2016-10-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationTitle | Chinese physics letters |
| PublicationTitleAlternate | Chinese Physics Letters |
| PublicationYear | 2016 |
| Publisher | Chinese Physical Society and IOP Publishing |
| Publisher_xml | – name: Chinese Physical Society and IOP Publishing |
| References | 11 12 16 Li H C (13) 2005; 22 1 2 4 5 Hossein H (3) 2007; 5 6 Yang Z L (15) 2005; 29 7 9 Cui W Z (14) 2004; 53 Kong D R (8) 2011; 28 10 |
| References_xml | – ident: 11 doi: 10.1016/j.physleta.2007.01.027 – volume: 5 start-page: 239 year: 2007 ident: 3 publication-title: J. Data Sci. – ident: 2 doi: 10.1098/rspa.2014.0409 – volume: 28 issn: 0256-307X year: 2011 ident: 8 publication-title: Chin. Phys. Lett. – volume: 22 start-page: 2776 issn: 0256-307X year: 2005 ident: 13 publication-title: Chin. Phys. Lett. doi: 10.1088/0256-307X/22/11/014 – volume: 29 start-page: 31 issn: 1000-3673 year: 2005 ident: 15 publication-title: Power Syst. Technol. – ident: 4 doi: 10.1142/S021812749800036X – ident: 7 doi: 10.1007/s10439-010-9933-5 – ident: 10 doi: 10.1103/PhysRevLett.59.845 – ident: 6 doi: 10.1016/j.asoc.2010.11.020 – volume: 53 start-page: 3303 issn: 0372-736X year: 2004 ident: 14 publication-title: Acta Phys. Sin. doi: 10.7498/aps.53.3303 – ident: 1 doi: 10.1016/0167-2789(92)90103-T – ident: 16 doi: 10.1007/s12209-015-2461-5 – ident: 9 doi: 10.1063/1.4812287 – ident: 12 doi: 10.1016/0167-2789(89)90074-2 – ident: 5 doi: 10.1016/j.jfranklin.2015.10.015 |
| SSID | ssj0011811 |
| Score | 2.1254375 |
| Snippet | We present a hybrid singular spectrum analysis （SSA） and fuzzy entropy method to filter noisy nonlinear time series. With this approach, SSA decomposes the... We present a hybrid singular spectrum analysis (SSA) and fuzzy entropy method to filter noisy nonlinear time series. With this approach, SSA decomposes the... |
| SourceID | crossref iop chongqing |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 19 |
| SubjectTerms | 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 |
| Title | Denoising Nonlinear Time Series Using Singular Spectrum Analysis and Fuzzy Entropy |
| URI | http://lib.cqvip.com/qk/84212X/201610/670332631.html https://iopscience.iop.org/article/10.1088/0256-307X/33/10/100501 |
| Volume | 33 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3fS8MwEA46EXzxtzinkgffpFvaNF36KLoxBaeog72Vpk1V1HZu3cP213vXtEMFEZGWUkIS0kua-5LcfUfIiZaoZhJmKRUJC9nvLOVHvhWytpM4GOWq8Hq_7nu9gXs1FJU1YeELk43Kqb8Jr4Yo2IiwNIiTLdTSuGUybHEOiXAzgR5cKxi-Egf55c3t4iABFFgRNK8qUzkJ_1gPciw8ZenjO2iOL7pqGdrzSfV0N4iqGm0sTl6a01w1o_k3Psd_fdUmWS-BKT0zBbbIkk63yWphIBpNdsjdhU6zZ9xYoH3DrhGOKfqPUNxf0xNa2B7Qe3igYSvFuPb5ePpGK9YTGqYx7U7n8xntoHX8aLZLBt3Ow3nPKuMxWBF3RG7JRMuERSpsK-1JH6CB0rCOVTFTHlPMC5G4BuBEHEnhaiVC3xW25tqHNQkP25zvkVqapXqfUNuLuevbsJxyYoCETCYAGz2GYJTFcNVJY9ELwcjwbgQezE6ANrldJ6LqlyAqqcwxosZrUBypSxmgNAOUZsC5SURp1klrUa6q9LcSp9BhQflfT37JffCn3A2yBujLM5aBh6QGnaKPAOHk6rgYwx8wFOgG |
| linkProvider | IOP Publishing |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bS8MwFD54QfHFuzjnJQ--Sbd0Wbr0UXTD6xQvsLfQtKmK2k23PWy_3nN6GSqIiLSUEpLQnrQ5X5Iv3wHYt4rcTMwdY0LpkPqdY_zQdwLeqMU1inKV7nq_bHsn9_WzjuxMQXOyF6bby7v-Ct5mQsGZCXNCnKqSl6Ypk05VCEzEk0vuVntRPA2zEjtkYnadXl1PFhPQiaWB84pyxUbhH-sinYXHbvLwht7ji7-axmf65H5aSxlNpJ-qFhLr5LkyHJhKOP6m6fjvN1uGxRygssOs0ApM2WQV5lKiaNhfg5tjm3SfaIKBtTOVjeCd0T4SRvNsts9SDgK7xQsRXBnFtx-8D19ZoX7CgiRireF4PGJNYsn3Rutw32reHZ04eVwGJxQ1OXBUbFXMQxM0jPWUjxDBWBzPmogbjxvuBSRgg7AiCpWsWyMDvy5dK6yPYxMRNITYgJmkm9hNYK4Xibrv4rCqFiE05CpG-OhxAqU8wqME5UlL6F6mv6E97KUQdQq3BLJoGx3mkuYUWeNFp0vrSmmyqCaLaiGyRLJoCaqTckWlv5U4wEbT-f_d_yX31p9y78H89XFLX5y2z8uwgIDMy8iC2zCD7WN3EPQMzG76SX8AEIHtag |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Denoising+Nonlinear+Time+Series+Using+Singular+Spectrum+Analysis+and+Fuzzy+Entropy&rft.jtitle=Chinese+physics+letters&rft.au=Jiang%2C+Jian&rft.au=Xie%2C+Hong-Bo&rft.date=2016-10-01&rft.issn=0256-307X&rft.eissn=1741-3540&rft.volume=33&rft.issue=10&rft.spage=100501&rft_id=info:doi/10.1088%2F0256-307X%2F33%2F10%2F100501&rft.externalDBID=n%2Fa&rft.externalDocID=10_1088_0256_307X_33_10_100501 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fimage.cqvip.com%2Fvip1000%2Fqk%2F84212X%2F84212X.jpg |