Denoising Non-Stationary Signals via Dynamic Multivariate Complex Wavelet Thresholding

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dc.citation.articleNumber1546
dc.citation.issueNumber11
dc.citation.journalTitleEntropy
dc.citation.volumeNumber25
dc.contributor.authorRaath, Kim C.
dc.contributor.authorEnsor, Katherine B.
dc.contributor.authorCrivello, Alena
dc.contributor.authorScott, David W.
dc.date.accessioned2023-12-15T16:04:40Z
dc.date.available2023-12-15T16:04:40Z
dc.date.issued2023
dc.description.abstractOver 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.
dc.identifier.citationRaath, 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
dc.identifier.doihttps://doi.org/10.3390/e25111546
dc.identifier.urihttps://hdl.handle.net/1911/115331
dc.language.isoeng
dc.publisherMDPI
dc.rightsExcept 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.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.keywordcontinuous wavelet transform
dc.subject.keyworddata-driven and adaptive thresholding
dc.subject.keywordpartial density estimation
dc.subject.keywordintegrated squared error
dc.subject.keywordWaveL2E
dc.subject.keywordnonparametric method
dc.titleDenoising Non-Stationary Signals via Dynamic Multivariate Complex Wavelet Thresholding
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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