A Wavelet Support Vector Machine Coupled Method for Time Series Prediction

Mabrouk, Anouar Ben; Kortas, Hedi; Dhifaoui, Zouhaier

doi:10.1142/s0219691308002719

Cited by 14 publications

(4 citation statements)

References 22 publications

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“…called the rescaled wavelet and scaling filters respectively. For backgrounds on wavelet analysis and especially its application in finance and economics the readers may refer to [41,[44][45][46][47][48][49][50][51][52][53][54][55].…”

Section: The Wavelet Methods For Textual Datamentioning

confidence: 99%

A Wavelet-Based Method for the Impact of Social Media on the Economic Situation: The Saudi Arabia 2030-Vision Case

2021

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In the present paper, a wavelet method is proposed to study the impact of electronic media on economic situation. More precisely, wavelet techniques are applied versus classical methods to analyze economic indices in the market. The technique consists firstly of filtering the data from unprecise circumstances (noise) to construct next a wavelet denoised contingency table. Next, a thresholding procedure is applied to such a table to extract the essential information porters. The resulting table subject finally to correspondence analysis before and after thresholding. As a case of study, the KSA 2030-vision is considered in the empirical part based on electronic and social media. Effects of the electronic media texts about the trading 2030 vision on the Saudi and global economy has been studied. Recall that the Saudi market is the most important representative market in the GCC continent. It has both regional and worldwide influence on economies and besides, it is characterized by many political, economic and financial movements such as the worldwide economic NEOM project. The findings provided in the present paper may be applied to predict the future situation of markets in GCC region and may constitute therefore a guide for investors to decide about investing or not in these markets.

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Section: The Wavelet Methods For Textual Datamentioning

confidence: 99%

A Wavelet-Based Method for the Impact of Social Media on the Economic Situation: The Saudi Arabia 2030-Vision Case

2021

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“…Also, we can mention the hybrid techniques developed by combining these ones. See Abhijith et al [2], AlMahamdya and Riley [3] and Babatunde [4], Ben Mabrouk et al [5]; Ho [6]; Mallat [7]; Xia and Suter [8]; Zemni et al [9,10]. Wavelet techniques start primarily by decomposing the signal, deciding the type of thresholding and reconstructing the signal.…”

Section: Introductionmentioning

confidence: 99%

A Novel Electrocardiogram Denoising Method based on Convex fused-Lasso Denoising with Non-Convex Regularization and Wavelet/Total Variation

Rezgui,

Talbi

2024

Preprint

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In this paper, we propose a new Electrocardiogram (ECG) denoising approach based on Convex fused lasso Denoising with non-convex regularization and Wavelet/Total Variation (WATV). This approach consists at first step of applying the Discrete Wavelet Transform (DWT) to the noisy ECG signal for obtaining a noisy approximation coefficient, cAb1 and a noisy details coefficient, cDb1. The latter is denoised by soft thresholding and we obtain a denoised details coefficient, cDd1. The second step of this approach consists of applying the DWT to cAb1 in order to obtain a noisy approximation coefficient, cAb2 and a noisy details coefficient, cDb2. The latter is denoised by Convex fused lasso denoising with non-convex regularization and we obtain a denoised details coefficient, cDd2. The coefficient, cAb2, is denoised by WATV based denoising technique and we obtain a denoised coefficient, cAd2. The inverse of DWT is then applied to cDd2 and cAd2 in order to obtain a denoised approximation coefficient, cAd1. The inverse of DWT is again applied to cDd1 and cAd1 for obtaining finally a denoised ECG signal. The performance of this proposed approach is proved by the computation of SNR, the PSNR, the MSE, the Mean Absolute Error (MAE), and the Cross-Correlation (CC).

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“…The wavelet methods derived from wavelet decompositions have be used for the numerical solution of differential equations, see [5,15,16]. And wavelet kernel support vector machine is shown to be successful in applications such as regression and classification, see [17,18,19,20,21,22]. So compare with Gaussian RBF based LSSVM for solving differential equations proposed in [8], wavelet kernel functions are more suitable for our multilevel LSSVM to approximate a solution with localized feathers or structures.…”

Section: Introductionmentioning

confidence: 99%

A Multilevel LSSVM for Elliptic Partial Differential Equations

Dong¹,

Liao²

2018

JAMCS

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In this paper, we propose a multilevel least square support vector machine (LSSVM) for solving elliptic boundary value problems based on wavelet kernel functions. This algorithm is constructed by a sequence of residual corrections and separating the computations of different levels, where different scale parameters are employed to accommodate different scales. In this multilevel algorithm, a coarse data set and a large scale parameter are chosen and the target function is interpolated in this data set to capture the large-scale variations of the target function at the first level, next, a smaller scale parameter is used to interpolate the residuals on a finer data set, capturing the finer details on the second level. The numerical tests on some linear second order elliptic boundary value problems show the efficiency of the multilevel algorithm.

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A Wavelet Support Vector Machine Coupled Method for Time Series Prediction

Cited by 14 publications

References 22 publications

A Wavelet-Based Method for the Impact of Social Media on the Economic Situation: The Saudi Arabia 2030-Vision Case

A Wavelet-Based Method for the Impact of Social Media on the Economic Situation: The Saudi Arabia 2030-Vision Case

A Novel Electrocardiogram Denoising Method based on Convex fused-Lasso Denoising with Non-Convex Regularization and Wavelet/Total Variation

A Multilevel LSSVM for Elliptic Partial Differential Equations

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