Denoising Non-Stationary Signals via Dynamic Multivariate Complex Wavelet Thresholding
| dc.citation.articleNumber | 1546 | en_US |
| dc.citation.issueNumber | 11 | en_US |
| dc.citation.journalTitle | Entropy | en_US |
| dc.citation.volumeNumber | 25 | en_US |
| dc.contributor.author | Raath, Kim C. | en_US |
| dc.contributor.author | Ensor, Katherine B. | en_US |
| dc.contributor.author | Crivello, Alena | en_US |
| dc.contributor.author | Scott, David W. | en_US |
| dc.date.accessioned | 2023-12-15T16:04:40Z | en_US |
| dc.date.available | 2023-12-15T16:04:40Z | en_US |
| dc.date.issued | 2023 | en_US |
| dc.description.abstract | Over the past few years, we have seen an increased need to analyze the dynamically changing behaviors of economic and financial time series. These needs have led to significant demand for methods that denoise non-stationary time series across time and for specific investment horizons (scales) and localized windows (blocks) of time. Wavelets have long been known to decompose non-stationary time series into their different components or scale pieces. Recent methods satisfying this demand first decompose the non-stationary time series using wavelet techniques and then apply a thresholding method to separate and capture the signal and noise components of the series. Traditionally, wavelet thresholding methods rely on the discrete wavelet transform (DWT), which is a static thresholding technique that may not capture the time series of the estimated variance in the additive noise process. We introduce a novel continuous wavelet transform (CWT) dynamically optimized multivariate thresholding method (𝑊𝑎𝑣𝑒𝐿2𝐸). Applying this method, we are simultaneously able to separate and capture the signal and noise components while estimating the dynamic noise variance. Our method shows improved results when compared to well-known methods, especially for high-frequency signal-rich time series, typically observed in finance. | en_US |
| dc.identifier.citation | Raath, Kim C., Ensor, Katherine B., Crivello, Alena, Scott, David W. (2023). Denoising Non-Stationary Signals via Dynamic Multivariate Complex Wavelet Thresholding. Entropy, 25(11), 1546. https://doi.org/10.3390/e25111546 | en_US |
| dc.identifier.doi | https://doi.org/10.3390/e25111546 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/115331 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | MDPI | en_US |
| dc.rights | Except where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license. Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.subject.keyword | continuous wavelet transform | en_US |
| dc.subject.keyword | data-driven and adaptive thresholding | en_US |
| dc.subject.keyword | partial density estimation | en_US |
| dc.subject.keyword | integrated squared error | en_US |
| dc.subject.keyword | WaveL2E | en_US |
| dc.subject.keyword | nonparametric method | en_US |
| dc.title | Denoising Non-Stationary Signals via Dynamic Multivariate Complex Wavelet Thresholding | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |
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