Wavelet analysis of covariance with application to atmospheric time series

Whitcher, Brandon; Guttorp, Peter; Percival, Donald B.

doi:10.1029/2000jd900110

Cited by 292 publications

(255 citation statements)

References 36 publications

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“…Note that the WCC is roughly analogous to its Fourier counterpart, the magnitude squared coherence, but the WCC is related to bands of scales (frequencies) [32]. The WCC ρ XY,τ λ j ∈ [−1, 1], and is used to determine lead/lag relationships between two time series on different scales λ j .…”

Section: The Wavelet Methodologymentioning

confidence: 99%

“…The WCC ρ XY,τ λ j ∈ [−1, 1], and is used to determine lead/lag relationships between two time series on different scales λ j . The confidence interval for the WCC is based on an extension of the classical result on the Fisher's Z-transformation of the correlation coefficient [32]. Thus, an approximate 100(1−2p)% confidence interval for the WCC is given by tanh{h…”

Section: The Wavelet Methodologymentioning

confidence: 99%

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Analyzing Crude Oil Spot Price Dynamics versus Long Term Future Prices: A Wavelet Analysis Approach

Polanco-Martínez

Abadie

2016

Energies

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Abstract:The West Texas Intermediate (WTI) spot price shows high volatility and in 2014 and 2015 when quoted prices declined sharply, long-term prices in future markets were less volatile. These prices are different and diverge depending on how they process fundamental and transitory factors. US tight oil production has been a major innovation with significant macroeconomic effects. In this paper we use WTI spot prices and long-term futures prices, the latter calculated as the expected value with a stochastic model calibrated with the futures quotes of each sample day. These long-term prices are the long-term equilibrium value under risk neutral measurement. In order to analyze potential time-scale relationships between spots and future, we perform a wavelet cross-correlation analysis using a novel wavelet graphical tool recently proposed. To check the direction of the causality, we apply non-linear causality tests to raw data and log returns as well as to the wavelet transform of the spot and futures prices. Our results show that in the spot and futures markets for the period 24 February 2006-2 April 2016 there is a bi-directional causality effect for most time scales (from intra-week to biannual). This suggests that spot and futures prices react simultaneously to new information.

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Section: The Wavelet Methodologymentioning

confidence: 99%

Section: The Wavelet Methodologymentioning

confidence: 99%

Analyzing Crude Oil Spot Price Dynamics versus Long Term Future Prices: A Wavelet Analysis Approach

Polanco-Martínez

Abadie

2016

Energies

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“…The periods for the calibration (1 October 1997 to 30 September 2001) and for the prediction (1 October 2001 to 31 August 2002) were the same as for the simple state-space model without decomposition. Previous analysis of the wavelet covariance between the observed and simulated data (see Whitcher et al [2000] for more details), have indicated that a maximum of five different levels is sufficient for these kind of data to capture the relevant error features. This method will be called MODWT-hindcast.…”

Section: Wavelet Transformation and Autocorrelation Shell Representationmentioning

confidence: 99%

Error‐correction methods and evaluation of an ensemble based hydrological forecasting system for the Upper Danube catchment

Bogner

Kalaš

2008

Atmospheric Science Letters

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Within the EU Project PREVention, Information and Early Warning (PREVIEW), ensembles of discharge series have been generated for the Danube catchment by the use of various weather forecast products. Hydrological models applied for streamflow prediction often have simulation errors that degrade forecast quality and limit the operational usefulness of the forecasts. Therefore, error-correction methods have been tested for adjusting the ensemble traces using a transformation derived with simulated and observed flows. This article presents first results of the combination of state-space models and wavelet transformations in order to update errors between the simulated (forecasted) and the observed discharge.

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“…Wavelet cross spectral analysis using the CWT has been applied to applications such as climatology (Maraun and Kurths (2004), Grinsted et al (2004)) and neuroscience (Lachaux et al, 2002). Wavelet cross-covariance and correlation has also been defined based on the maximal overlap discrete wavelet transform (MODWT) (see Whitcher et al (2000) and Serroukh and Walden (2000)). Their formulation assumes that the d'th order backwards differences of the series can be modelled as a stationary process.…”

Section: Introductionmentioning

confidence: 99%

Estimating linear dependence between nonstationary time series using the locally stationary wavelet model

2010

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Large volumes of neuroscience data comprise multiple, non-stationary electrophysiological or neuroimaging time series recorded from different brain regions. Estimating the dependence between such neural time series accurately is critical, since changes in the dependence structure are presumed to reflect functional interactions between neuronal populations. We propose a new method of wavelet coherence, derived from the new bivariate locally stationary wavelet (LSW) time series model. Since wavelets are localised in both time and scale, this approach leads to a natural, local and multiscale estimate of nonstationary dependence. Our methodology is illustrated by application to a simulated example, and to electrophysiological data relating to interactions between the rat hippocampus and prefrontal cortex during working memory and decision-making. We thereby demonstrate that this novel LSW model can be of use in systems neuroscience applications.

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Wavelet analysis of covariance with application to atmospheric time series

Cited by 292 publications

References 36 publications

Analyzing Crude Oil Spot Price Dynamics versus Long Term Future Prices: A Wavelet Analysis Approach

Analyzing Crude Oil Spot Price Dynamics versus Long Term Future Prices: A Wavelet Analysis Approach

Error‐correction methods and evaluation of an ensemble based hydrological forecasting system for the Upper Danube catchment

Estimating linear dependence between nonstationary time series using the locally stationary wavelet model

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