| Autor: |
Shea, Daniel E., Giridharagopal, Rajiv, Ginger, David S., Brunton, Steven L., Kutz, J. Nathan |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IEEE Access, vol. 9, pp. 83453-83466, 2021 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/ACCESS.2021.3087595 |
| Popis: |
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 {\em 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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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