Analisis Indeks Harga Saham Gabungan (IHSG) Menggunakan Metode Wavelet Thresholding

In order to improve the effects of denoising, this paper introduces the basic principles of wavelet threshold denoising and traditional structures threshold functions. Meanwhile, it proposes wavelet threshold function and fixed threshold formula which are both improved here. First, this paper studies the problems existing in the traditional wavelet threshold functions and introduces the adjustment factors to construct the new threshold function basis on soft threshold function. Then, it studies the fixed threshold and introduces the logarithmic function of layer number of wavelet decomposition to design the new fixed threshold formula. Finally, this paper uses hard threshold, soft threshold, Garrote threshold, and improved threshold function to denoise different signals. And the paper also calculates signal-to-noise (SNR) and mean square errors (MSE) of the hard threshold functions, soft thresholding functions, Garrote threshold functions, and the improved threshold function after denoising. Theoretical analysis and experimental results showed that the proposed approach could improve soft threshold functions with constant deviation and hard threshold with discontinuous function problems. The proposed approach could improve the different decomposition scales that adopt the same threshold value to deal with the noise problems, also effectively filter the noise in the signals, and improve the SNR and reduce the MSE of output signals.

For the problem of denoising incomplete phenomenon appeared in the existing wavelet threshold methods, a new kind of threshold function and denoising method of parameter adjustable wavelet threshold is proposed. The traditional hard threshold and soft threshold wavelet had problems such as continuity and constant deviation. Firstly, to construct a new threshold function and theoretically proves the superiority of this new threshold function, as long as choosing the suitable parameter, the new threshold function has a better denoising effect than traditional thresholding function. Secondly, to reduce the deviation between wavelet coefficients and the original, the threshold value is changed with the change of decomposition scale. The experimental results demonstrate that by using the proposed method, whether the Gaussian noise or impulse noise, the signal-to-noise ratio (SNR) of image and the peak signal to noise ratio (PSNR) of image can be greatly improved and the mean square error (MSE) of the image was reduced, the denoising effect is better than other algorithms.

A large amount of noise is mixed in the actual process industrial data, which leads to a decline in data quality, which in turn affects the diagnosis results. In reality, the wavelet threshold method is generally used to remove noise. However, the hard threshold method which is in the wavelet threshold method has the disadvantages of poor signal continuity and signal oscillating. The soft threshold method has the disadvantages of constant deviation and large error of reconstructed signal. To this end, we propose a random soft-hard threshold algorithm which can combine hard and soft thresholds. Then the new algorithm is combined with the principal component analysis for fault diagnosis of the TE (Tennessee Eastman) process. The MATLAB simulation results show that the above method can effectively denoise, identify and detect faults more accurately, and can better find the variables that have the greatest impact on the fault.

The stationary data tests in time series data are the most common idea to create the model with an autoregressive (AR) model, moving average (MA) model, autoregressive moving average (ARMA) model, and autoregressive integrated moving average (ARIMA) model. In this chapter, we present the tseries package in the R program to investigate stationary data on the time series. This package offers Augmented Dickey-Fuller (ADF), Kwiatkowski-Phillips-Schmidt-Shin (KPSS), and Phillips-Perron (PP) tests. Three tests also provide the statistics test and p-value to decide the acceptance or rejection of the null hypothesis. This chapter also aims to present the calculation of these tests proposes step-by-step and the available coding commands under similar time series data. The performance of these tests considers the probability of type I error and the power of the test. The time series data in this study is simulated from the stationary in term of random walk process and non-stationary in term of MA model. This simulation is also conducted to recommend that the user select the appropriate stationarity tests. The R program employs to simulate and analyzes data on 1,000 replications for several sample sizes. The simulation results showed that the ADF and PP tests could control the best probability of type I error on non-stationary time series data. For the power of the test, ADF and PP tests give the highest power of the test on stationary time series data.

In this paper, a localized wavelet thresholding strategy which adopts context-based thresholding operators is proposed. Traditional wavelet thresholding methods, such as VisuShrink, LevelShrink and BayesShrink, apply the conventional hard and soft thresholding operators and only differ in the selection of the threshold. The conventional soft and hard thresholding operators are point operators in the sense that only the value of the processed wavelet coefficient is taken into consideration before thresholding it. In this work, it will be shown that the performance of some of the standard wavelet thresholding methods can be improved by applying a localized, context-based, thresholding strategy instead of the conventional thresholding operators.

Based on multi-resolution analysis of wavelet, this article is aimed at building a new soft threshold function for wavelet de-noising, to overcome the discontinuous disadvantage of the hard threshold function. In the area adjacent to the threshold, continuously adjustable nonlinear functions are introduced in piecewise to process the wavelet coefficients more carefully in this area. Consequently, large deviation caused by super-compression of wavelet coefficients when using traditional soft threshold could be avoided, and the nonlinearity of the system is able to be effectively kept. Using both signal to noise ratio (SNR) and mean square error (MSE) as the evaluation indicators, simulation results show that the improved method is more effective than the method based on traditional hard and soft threshold.

Aiming at the problems of weak ECG signal (ECG), low frequency range and incomplete noise removal, a wavelet threshold denoising algorithm based on optimized VMD (Variational Modal Decomposition) is proposed. This method first reads the ECG signal, and uses genetic algorithm (GA) to optimize the two parameters of the VMD decomposition level and the penalty factor in the parameter search space. After the parameters are optimized, the ECG signal is decomposed by VMD, and a series of decompositions are obtained. Modal component (u), judge the main component of each modal component according to the correlation coefficient of each modal component and the noisy ECG signal, improve the wavelet threshold denoising of the noise-dominated modal component, and denoise the denoised modal The component and the modal component dominated by the ECG signal are inversely transformed and reconstructed to obtain the denoised ECG signal. Use the real ECG signal in the MIT-BIH database to conduct experiments, and compare the wavelet threshold denoising method based on optimized VMD with the wavelet threshold method alone and the wavelet threshold method based on VMD. The results show that the method has better denoising effect. good.

With the development of internet of things technology community time-series data modeling is a new hot topic of scholars all over the world, modeling ability with its artificial neural network under the environment of Internet of things technology community complex nonlinear time series data system and the application ability good data, application and network in nonlinear time series data forecasting technology community environment get massive practice. The subject of the nonlinear network technology community time-series data prediction several bottlenecks exist in neural network analysis, artificial neural network is presented from the perspective of nonlinearity IOT application of forecast technology community time-series data to optimize the prediction bottleneck neural network environment on the based. Therefore, through the simulation community data of artificial neural network show that the practicality and efficiency of the algorithm.

Aiming at the problem that the signals received by MEMS vector hydrophones are mixed with a large amount of external environmental noise, and inevitably produce baseline drift and other distortion phenomenons which made it difficult for the further signal detection and recognition, a joint denoising method (VMD-NWT) based on variational mode decomposition (VMD) and nonlinear wavelet threshold (NWT) processing is proposed. The main frequency of the noisy signal is first obtained by Fourier transform. Then the noisy signal is decomposed by VMD to obtain the IMF components. The center frequency and correlation coefficient of each IMF component further determine that the IMF components belong to noise IMF components, noisy IMF components or pure IMF components. Then the pure IMF components are reserved, the noise IMF components are removed, and the noisy IMF components are denoised by NWT processing method with new threshold function as a whole. Finally, the denoised IMF components and the pure IMF components are reconstructed to obtain the denoised signal to realize the extraction of useful signals and baseline drift removal. Compared with complete ensemble empirical mode decomposition with adaptive noise combined with wavelet threshold processing method (CEEMDAN-WT), ensemble empirical mode decomposition combined with wavelet threshold processing method (EEMD-WT), and single wavelet threshold processing method with compromised function between hard and soft threshold (ZWT), the VMD-NWT in this paper has the advantages of less calculation and simple implementation. The simulation comparison experiments verify that the VMD-NWT is superior to CEEMDAN-WT, EEMD-WT, and ZWT. Then VMD-NWT is applied to process the measured data obtained from the Fenji experiment conducted by the North University of China. Simulation and lake trial results show that VMD-NWT has better denoising effect and can realize baseline drift removal. So the proposed VMD-NWT has certain practical research value.

Based on the Multi-analysis wavelet threshold denosing method which is put forward by D. L. Dohono and I. M. Johnstone, a new threshold function is posed. This new threshold function has many advantages over the classical soft and hard threshold function. It is clear in physics meaning and simple in expression. The experimental results show that this improved method is effective in removing white noise, and gives better RMSE performance and SNR gains than soft and hard threshold methods, and makes it possible to embody the advantages of the wavelet threshold denoising method more fully.

Electric energy signals are often contaminated by noise during the collection process, which affects the accurate positioning of disturbances. The discontinuity of traditional hard threshold functions can lead to oscillations, while excessive smoothness of soft threshold functions can lead to signal distortion. The optimized threshold function and scale adaptive processing on the selection of the threshold are proposed. The wavelet thresholding method is used to process voltage sag, rise, and interrupt signals containing noise. The denoised signal is decomposed into five scale wavelets using db7 wavelet and detected by using wavelet transform modulus maximum and signal singularity principle. Experiments have shown that optimized threshold and threshold function not only effectively denoise but also retain the effective mutation point information in the original signal, and the new method is reliable in detection.

In view of the wheel state monitoring, the wheel vibration signal is often used to reflect the wheel state information, but the original vibration signal contains a lot of noise. In order to extract the effective wheel information, it is necessary to reduce the noise of the original vibration signal. The problems existing in the traditional wavelet threshold denoising method include unreasonable threshold setting, lack of continuity of hard threshold function and fixed deviation of wavelet coefficients processed by soft threshold function. In this paper, the traditional wavelet threshold denoising method is improved from threshold selection and threshold function, and an improved wavelet threshold denoising method is proposed. This method is used to reduce the noise of the measured wheelset vibration signal and compared with the traditional wavelet denoising method from evaluation indexes and graphical results. The results show that the improved wavelet threshold denoising method has better noise reduction effect.

Depth of anaesthesia (DoA) is an important measure for assessing the degree to which the central nervous system of a patient is depressed by a general anaesthetic agent, depending on the potency and concentration with which anaesthesia is administered during surgery. We can monitor the DoA by observing the patient’s electroencephalography (EEG) signals during the surgical procedure. Typically high frequency EEG signals indicates the patient is conscious, while low frequency signals mean the patient is in a general anaesthetic state. If the anaesthetist is able to observe the instantaneous frequency changes of the patient’s EEG signals during surgery this can help to better regulate and monitor DoA, reducing surgical and post-operative risks. This paper describes an approach towards the development of a 3D real-time visualization application which can show the instantaneous frequency and instantaneous amplitude of EEG simultaneously by using empirical mode decomposition (EMD) and the Hilbert–Huang transform (HHT). HHT uses the EMD method to decompose a signal into so-called intrinsic mode functions (IMFs). The Hilbert spectral analysis method is then used to obtain instantaneous frequency data. The HHT provides a new method of analyzing non-stationary and nonlinear time series data. We investigate this approach by analyzing EEG data collected from patients undergoing surgical procedures. The results show that the EEG differences between three distinct surgical stages computed by using sample entropy (SampEn) are consistent with the expected differences between these stages based on the bispectral index (BIS), which has been shown to be quantifiable measure of the effect of anaesthetics on the central nervous system. Also, the proposed filtering approach is more effective compared to the standard filtering method in filtering out signal noise resulting in more consistent results than those provided by the BIS. The proposed approach is therefore able to distinguish between key operational stages related to DoA, which is consistent with the clinical observations. SampEn can also be viewed as a useful index for evaluating and monitoring the DoA of a patient when used in combination with this approach.

The magnetic flux leakage (MFL) evaluation is often used for the overhauling of oil extracting operation in the oil field to realize the real-time damage assessment of the pipeline. Since the MFL signal is affected by various noise sources in the field, this paper introduces the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN). On the basis of this, a particle swarm optimization wavelet threshold (PSO-WT) method is proposed, and the signal reconstruction option is improved to extract the leakage magnetic flux signal of tubing defects. First, CEEMDAN is used to add pairs of positive and negative white noise to the MFL signal, and then the signal is decomposed into several intrinsic mode functions (IMFs). Second, the correlation coefficient selection limit is defined. Taking into account the characteristics of the decomposed signal, the useless IMFs and useful IMFs are selected from the IMF components, where some of the useful IMF components contain less noise. Third, the PSO-WT algorithm is combined to further filter the noisy and useful IMF components. Finally, the filtered IMF components and the pure useful IMF components are selected to reconstruct the signal. In the experiment, the ensemble empirical mode decomposition (EEMD) method and CEEMDAN are used to decompose the noisy MFL signals ensemble in the field. The MFL signal is reconstructed under the correlation coefficient selection. It can be seen from the comparison of EEMD that the MFL signal is reconstructed under the same conditions after CEEMDAN decomposition, and its signal-to-noise ratio is increased by 8%. At the same time, after CEEMDAN decomposition, the selected noisy useful IMFs are further filtered by the wavelet threshold (WT) method and the PSO-WT method. Also, it indicates that the reconstructed signal processed by PSO-WT is 17% higher than the reconstructed signal after WT processing.

This study aims to determine the effect of inflation, exchange value, interest rate, and gross domestic product against the Indonesia Composite Index (ICI). The type of research used explanatory research, with quantitative approach and took all time series data covering inflation, exchange value, interest rate, and gross domestic product and Indonesia Composite Index (ICI) for the period of 2000 until 2024. The data analysis used multiple linear regression analysis using SPSS 26. The results of this study indicate that the value of coefficient of determination(R2) 84.9% which means independent variables affect the dependent variable 84.9% and the rest is 15.1% influenced by other variables outside this study. F test results indicate that the independent variables of inflation, exchange value, interest rate, and gross domestic product simultaneously have significant effect on the Indonesia Composite Index (ICI). The result of t test shows that the inflation variable partially positively influenced the Indonesia Composite Index(ICI), exchange rate variable partially have significant positive effect on Indonesia Composite Index, interest rate variable partially have significant negative effect on Indonesia Composite Index(ICI), , while GDP partially have positif effect on Indonesia Composite Index(ICI).