Analysis of past surface temperature reconstructions based on the tree-ring chronologies and borehole temperature measurements

Nagornov, O. V.; Nikitaev, V. G.; Pronichev, A. N.; Tyuflin, S. A.; Bukharova, T. I.

doi:10.1063/1.4952015

Cited by 4 publications

(2 citation statements)

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“…To obtain the best decomposition depth, the Hurst index is calculated from the first level of detail, followed by an increase based on the Haar wavelet [32,33]. The results are shown in Table 2.…”

Section: The Optimal Level Of Detail Based On the Hurst Indexmentioning

confidence: 99%

“…It can be seen, that the value of the Hurst index undergoes minor variation with an increase in the level of detail. The next step to get the Hurst index as a function of the level of detail was tested, replacing the mother wavelet with the Daubechies wavelet with a number of zero moments equal to 4 [32,33], which provides a more smooth filtering of noise. The results of the wavelet decomposition are presented in Table 3.…”

Section: The Optimal Level Of Detail Based On the Hurst Indexmentioning

confidence: 99%

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A mathematical model for estimating and forecasting the state of a digital substation based on the wavelet transform method

et al. 2017

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Abstract. The article the possibility of applying the wavelet transform method in combination with a neural-fuzzy approach to solving problems of forecasting the state of digital substations is considered. The optimum level of the wavelet expansion of the time series corresponding to the change in the phase voltage for the day on the basis of the Hurst index is determined. The influence of the sample size and the type of the mother wavelet on the Hurst index is researched. It was revealed, that for wavelet decomposition, the use of the Daubechy wavelet as the mother wavelet is effective, which provides a smoother filtering of noise, compared to the Haar wavelet. Analysis of the original series does not allow to evaluate the optimal level of wavelet expansion if the noise level of the time series under consideration is low (less than 10%), since the Hurst index remains unchanged. However, using the logarithm of changing the time series allows for small fluctuations to be taken into account, which allows to determine the optimal level of the wavelet expansion for their smoothing.

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Section: The Optimal Level Of Detail Based On the Hurst Indexmentioning

confidence: 99%

Section: The Optimal Level Of Detail Based On the Hurst Indexmentioning

confidence: 99%