Browsing by Subject "wavelets"

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Access status: Open Access ,
Identyfikacja tłumienia w gruncie
(2008) Wrana, Bogumił; Czado, Bartłomiej
The first part of this paper describes a typical procedure of determining damping coefficients of a construction, using modal method. A short analysis of application of this method to soil is discussed. In the main part a procedure of determinig the damping of soil using half-power bandwidth method and wavelet transformation is described. Some basic andvantages of this method referring to modal method were shown. Final part of the paper contains a numerical example of application of the described method.
Access status: Open Access ,
Inversion of the Riemann-Liouville operator and its dual using wavelets
(2015) Baccar, Cyrine; Hamadi, Nadia Ben; Herch, Hajer; Meherzi, Fatma
We define and study the generalized continuous wavelet transform associated with the Riemann-Liouville operator that we use to express the new inversion formulas of the Riemann-Liouville operator and its dual.
Access status: Open Access ,
Wavelet-based forecasting of ARIMA time series - an empirical comparison of different methods
(2014) Schlüter, Stephan; Deuschle, Carola
By means of wavelet transform, an ARIMA time series can be split into different frequency components. In doing so, one is able to identify relevant patters within this time series, and there are different ways to utilize this feature to improve existing time series forecasting methods. However, despite a considerable amount of literature on the topic, there is hardly any work that compares the different wavelet-based methods with each other. In this paper, we try to close this gap. We test various wavelet-based methods on four data sets, each with its own characteristies. Eventually we come to the conclusion that using wavelets does improve forecasting quality especially for time horizons longer than one-day-ahead. However, there is no single superior method: either wavelet-based denoising or wavelet-based time series decomposition is best. Performance depends on the data set as well as the forecasting time horizon.