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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.05012 |
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| _version_ | 1866909377460436992 |
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| author | McGonigle, Euan T. Killick, Rebecca Nunes, Matthew A. |
| author_facet | McGonigle, Euan T. Killick, Rebecca Nunes, Matthew A. |
| contents | The TrendLSW R package has been developed to provide users with a suite of wavelet-based techniques to analyse the statistical properties of nonstationary time series. The key components of the package are (a) two approaches for the estimation of the evolutionary wavelet spectrum in the presence of trend; and (b) wavelet-based trend estimation in the presence of locally stationary wavelet errors via both linear and nonlinear wavelet thresholding; and (c) the calculation of associated pointwise confidence intervals. Lastly, the package directly implements boundary handling methods that enable the methods to be performed on data of arbitrary length, not just dyadic length as is common for wavelet-based methods, ensuring no pre-processing of data is necessary. The key functionality of the package is demonstrated through two data examples, arising from biology and activity monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05012 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TrendLSW: Trend and Spectral Estimation of Nonstationary Time Series in R McGonigle, Euan T. Killick, Rebecca Nunes, Matthew A. Methodology Computation The TrendLSW R package has been developed to provide users with a suite of wavelet-based techniques to analyse the statistical properties of nonstationary time series. The key components of the package are (a) two approaches for the estimation of the evolutionary wavelet spectrum in the presence of trend; and (b) wavelet-based trend estimation in the presence of locally stationary wavelet errors via both linear and nonlinear wavelet thresholding; and (c) the calculation of associated pointwise confidence intervals. Lastly, the package directly implements boundary handling methods that enable the methods to be performed on data of arbitrary length, not just dyadic length as is common for wavelet-based methods, ensuring no pre-processing of data is necessary. The key functionality of the package is demonstrated through two data examples, arising from biology and activity monitoring. |
| title | TrendLSW: Trend and Spectral Estimation of Nonstationary Time Series in R |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2406.05012 |