Extraction of Instantaneous Frequencies and Amplitudes in Nonstationary Time-Series Data
Time-series analysis is critical for a diversity of applications in science and engineering. By leveraging the strengths of modern gradient descent algorithms, the Fourier transform, multi-resolution analysis, and Bayesian spectral analysis, we propose a data-driven approach to time-frequency analys...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9448199/ |
| _version_ | 1818450378549624832 |
|---|---|
| author | Daniel E. Shea Rajiv Giridharagopal David S. Ginger Steven L. Brunton J. Nathan Kutz |
| author_facet | Daniel E. Shea Rajiv Giridharagopal David S. Ginger Steven L. Brunton J. Nathan Kutz |
| author_sort | Daniel E. Shea |
| collection | DOAJ |
| description | Time-series analysis is critical for a diversity of applications in science and engineering. By leveraging the strengths of modern gradient descent algorithms, the Fourier transform, multi-resolution analysis, and Bayesian spectral analysis, we propose a data-driven approach to time-frequency analysis that circumvents many of the shortcomings of classic approaches, including the extraction of nonstationary signals with discontinuities in their behavior. The method introduced is equivalent to a nonstationary Fourier mode decomposition (NFMD) for nonstationary and nonlinear temporal signals, allowing for the accurate identification of instantaneous frequencies and their amplitudes. The method is demonstrated on a diversity of time-series data, including on data from cantilever-based electrostatic force microscopy to quantify the time-dependent evolution of charging dynamics at the nanoscale. |
| first_indexed | 2024-12-14T20:50:21Z |
| format | Article |
| id | doaj.art-28c3e1f29d8e4e8daf279d3ce0e6dcfb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T20:50:21Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-28c3e1f29d8e4e8daf279d3ce0e6dcfb2022-12-21T22:47:50ZengIEEEIEEE Access2169-35362021-01-019834538346610.1109/ACCESS.2021.30875959448199Extraction of Instantaneous Frequencies and Amplitudes in Nonstationary Time-Series DataDaniel E. Shea0https://orcid.org/0000-0002-0351-1629Rajiv Giridharagopal1https://orcid.org/0000-0001-6076-852XDavid S. Ginger2Steven L. Brunton3https://orcid.org/0000-0002-6565-5118J. Nathan Kutz4https://orcid.org/0000-0002-6004-2275Department of Materials Science and Engineering, University of Washington, Seattle, WA, USADepartment of Chemistry, University of Washington, Seattle, WA, USADepartment of Chemistry, University of Washington, Seattle, WA, USADepartment of Mechanical Engineering, University of Washington, Seattle, WA, USADepartment of Applied Mathematics, University of Washington, Seattle, WA, USATime-series analysis is critical for a diversity of applications in science and engineering. By leveraging the strengths of modern gradient descent algorithms, the Fourier transform, multi-resolution analysis, and Bayesian spectral analysis, we propose a data-driven approach to time-frequency analysis that circumvents many of the shortcomings of classic approaches, including the extraction of nonstationary signals with discontinuities in their behavior. The method introduced is equivalent to a nonstationary Fourier mode decomposition (NFMD) for nonstationary and nonlinear temporal signals, allowing for the accurate identification of instantaneous frequencies and their amplitudes. The method is demonstrated on a diversity of time-series data, including on data from cantilever-based electrostatic force microscopy to quantify the time-dependent evolution of charging dynamics at the nanoscale.https://ieeexplore.ieee.org/document/9448199/Signal analysisparameter estimationfrequency estimationamplitude estimationspectral analysissignal processing algorithms |
| spellingShingle | Daniel E. Shea Rajiv Giridharagopal David S. Ginger Steven L. Brunton J. Nathan Kutz Extraction of Instantaneous Frequencies and Amplitudes in Nonstationary Time-Series Data IEEE Access Signal analysis parameter estimation frequency estimation amplitude estimation spectral analysis signal processing algorithms |
| title | Extraction of Instantaneous Frequencies and Amplitudes in Nonstationary Time-Series Data |
| title_full | Extraction of Instantaneous Frequencies and Amplitudes in Nonstationary Time-Series Data |
| title_fullStr | Extraction of Instantaneous Frequencies and Amplitudes in Nonstationary Time-Series Data |
| title_full_unstemmed | Extraction of Instantaneous Frequencies and Amplitudes in Nonstationary Time-Series Data |
| title_short | Extraction of Instantaneous Frequencies and Amplitudes in Nonstationary Time-Series Data |
| title_sort | extraction of instantaneous frequencies and amplitudes in nonstationary time series data |
| topic | Signal analysis parameter estimation frequency estimation amplitude estimation spectral analysis signal processing algorithms |
| url | https://ieeexplore.ieee.org/document/9448199/ |
| work_keys_str_mv | AT danieleshea extractionofinstantaneousfrequenciesandamplitudesinnonstationarytimeseriesdata AT rajivgiridharagopal extractionofinstantaneousfrequenciesandamplitudesinnonstationarytimeseriesdata AT davidsginger extractionofinstantaneousfrequenciesandamplitudesinnonstationarytimeseriesdata AT stevenlbrunton extractionofinstantaneousfrequenciesandamplitudesinnonstationarytimeseriesdata AT jnathankutz extractionofinstantaneousfrequenciesandamplitudesinnonstationarytimeseriesdata |