Journal Article10.1142/S0219691308002719
A wavelet support vector machine coupled method for time series prediction
19
TL;DR: A hybrid scheme for time series prediction is developed based on wavelet decomposition combined with Bayesian Least Squares Support Vector Machine regression.
read more
Abstract: In this paper, a hybrid scheme for time series prediction is developed based on wavelet decomposition combined with Bayesian Least Squares Support Vector Machine regression. As a filtering step, us...
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A wavelet method coupled with quasi-self-similar stochastic processes for time series approximation
TL;DR: In the present paper, some existing models are reviewed and modified, based on wavelet theory and self-similarity, to recover multi-scaling cases for approximating financial signals.
24
Toward new multi-wavelets: associated filters and algorithms. Part I: theoretical framework and investigation of biomedical signals, ECG, and coronavirus cases.
Malika Jallouli,Makerem Zemni,Anouar Ben Mabrouk,Anouar Ben Mabrouk,Anouar Ben Mabrouk,Mohamed Ali Mahjoub +5 more
- 06 Sep 2021
TL;DR: In this paper, the authors proposed an extension of the wavelet concept to multi-wavelets and applied it to the detection of new pandemics threatening the humanity such as the new coronavirus.
•Posted Content
Wavelet-Based Prediction for Governance, Diversification and Value Creation Variables
TL;DR: In this article, the authors study the possibility of completing data bases of a sample of governance, diversification and value creation variables by providing a well adapted method to reconstruct the missing parts in order to obtain a complete sample to be applied for testing the ownership-structure/diversification relationship.
10
Wavelet kernel function based multiscale LSSVM for elliptic boundary value problems
TL;DR: A multilevel algorithm is introduced, which decompose the multiscale algorithm into multiple levels, and the numerical tests on some linear second order elliptic boundary value problems show the efficiency of the multileVEL algorithm and the adaptive algorithm.
9
Wavelet autoregressive forecasting of climatic parameters for photovoltaic systems
Chokri Ben Salah,Anouar Ben Mabrouk,Mohamed Ouali +2 more
- 22 Mar 2011
TL;DR: In this article, wavelet decomposition is combined with autoregressive models to analyze and predict climate time series such as solar radiation and photovoltaic cell temperature, and the work effectiveness is evaluated by the prediction of the electric energy produced by a 100 Wp photoprocessor.
9
References
•Book
The Nature of Statistical Learning Theory
Vladimir Vapnik
- 01 Jan 1995
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
46K
Statistical learning theory
Vladimir Vapnik
- 01 Jan 1998
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
30.4K
A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Ten Lectures on Wavelets
TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.
14.2K
Least Squares Support Vector Machine Classifiers
TL;DR: A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.