Denoising Non-stationary Signals by Dynamic Multivariate Complex Wavelet Thresholding

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dc.contributor.authorRaath, Kim
dc.contributor.authorEnsor, Katherine B.
dc.contributor.authorScott, David W.
dc.contributor.authorCrivello, Alena
dc.date.accessioned2020-09-11T01:24:30Z
dc.date.available2020-09-11T01:24:30Z
dc.date.issued2020
dc.description.abstractOver the past few years, we have seen an increased need for analyzing 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 transforms (DWT), 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. 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. Supplementary materials for your article are available online.
dc.format.extent28 pp
dc.identifier.citationRaath, Kim, Ensor, Katherine B., Scott, David W., et al.. "Denoising Non-stationary Signals by Dynamic Multivariate Complex Wavelet Thresholding." (2020) SSRN: http://dx.doi.org/10.2139/ssrn.3528714.
dc.identifier.doihttp://dx.doi.org/10.2139/ssrn.3528714
dc.identifier.urihttps://hdl.handle.net/1911/109333
dc.language.isoeng
dc.publisherSSRN
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 by Dynamic Multivariate Complex Wavelet Thresholding
dc.typepapers (documents)
dc.type.dcmiText
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