A wavelet “time-shift-detail” decomposition

A wavelet "time-shift-detail" decomposition

Chaotic Time Series Prediction Using Wavelet Decomposition

A novel approach to chaotic time series prediction is proposed. It is based on the use of the Discrete Wavelet Transform for obtaining a proper decomposition of the original sequence and standard multilayer neural networks for performing the prediction of the individual components. Simulation results for the case of chaotic signals obtained by integrating the Lorenz equations are presented, and...

Wavelet Time Shift Properties Integration with Support Vector Machines

This paper presents a short evaluation about the integration of information derived from wavelet non-linear-time-invariant (nonLTI) projection properties using Support Vector Machines (SVM). These properties may give additional information for a classifier trying to detect known patterns hidden by noise. In the experiments we present a simple electromagnetic pulsed signal recognition scheme, wh...

Shift Invariant Wavelet Packet

In this work, a shifted wavelet packet (SWP) library, containing all the time shifted wavelet packet bases, is deened. A corresponding shift-invariant wavelet packet decomposition (SIWPD) search algorithm for a \best basis" is introduced. The search algorithm is representable by a binary tree, in which a node symbolizes an appropriate subspace of the original signal. We prove that the resultant...

Shift-orthogonal wavelet bases

Shift-orthogonal wavelets are a new type of multiresolution wavelet bases that are orthogonal with respect to translation (or shifts) within one level but not with respect to dilations across scales. In this paper, we characterize these wavelets and investigate their main properties by considering two general construction methods. In the first approach, we start by specifying the analysis and s...