Generalized Volterra-Wiener and surrogate data methods for complex time series analysis
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2008
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| Online Access: | http://hdl.handle.net/1721.1/41619 |
| _version_ | 1826217449907814400 |
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| author | Shashidhar, Akhil |
| author2 | Chi-Sang Poon. |
| author_facet | Chi-Sang Poon. Shashidhar, Akhil |
| author_sort | Shashidhar, Akhil |
| collection | MIT |
| description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006. |
| first_indexed | 2024-09-23T17:03:50Z |
| format | Thesis |
| id | mit-1721.1/41619 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T17:03:50Z |
| publishDate | 2008 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/416192019-04-12T16:07:52Z Generalized Volterra-Wiener and surrogate data methods for complex time series analysis Shashidhar, Akhil Chi-Sang Poon. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006. Includes bibliographical references (leaves 133-150). This thesis describes the current state-of-the-art in nonlinear time series analysis, bringing together approaches from a broad range of disciplines including the non-linear dynamical systems, nonlinear modeling theory, time-series hypothesis testing, information theory, and self-similarity. We stress mathematical and qualitative relationships between key algorithms in the respective disciplines in addition to describing new robust approaches to solving classically intractable problems. Part I presents a comprehensive review of various classical approaches to time series analysis from both deterministic and stochastic points of view. We focus on using these classical methods for quantification of complexity in addition to proposing a unified approach to complexity quantification encapsulating several previous approaches. Part II presents robust modern tools for time series analysis including surrogate data and Volterra-Wiener modeling. We describe new algorithms converging the two approaches that provide both a sensitive test for nonlinear dynamics and a noise-robust metric for chaos intensity. by Akhil Shashidhar. M.Eng. 2008-05-19T16:01:42Z 2008-05-19T16:01:42Z 2006 2006 Thesis http://hdl.handle.net/1721.1/41619 216883415 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 150 leaves application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Shashidhar, Akhil Generalized Volterra-Wiener and surrogate data methods for complex time series analysis |
| title | Generalized Volterra-Wiener and surrogate data methods for complex time series analysis |
| title_full | Generalized Volterra-Wiener and surrogate data methods for complex time series analysis |
| title_fullStr | Generalized Volterra-Wiener and surrogate data methods for complex time series analysis |
| title_full_unstemmed | Generalized Volterra-Wiener and surrogate data methods for complex time series analysis |
| title_short | Generalized Volterra-Wiener and surrogate data methods for complex time series analysis |
| title_sort | generalized volterra wiener and surrogate data methods for complex time series analysis |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/41619 |
| work_keys_str_mv | AT shashidharakhil generalizedvolterrawienerandsurrogatedatamethodsforcomplextimeseriesanalysis |