An Urban Metro Section Flow Forecasting Method Combining Time Series Decomposition and a Generative Adversarial Network

Short-term traffic prediction based on time series decomposition

Financial time series forecasting has been a challenge for time series analysts and researchers because it is noisy, nonstationary and chaotic. To overcome this limitation, this study uses empirical mode decomposition (EMD) and phase space reconstruction (PSR) to assist in the task of financial time series forecasting. In addition, we propose an approach that combines these two data preprocessing methods with extreme learning machine (ELM). The approach contains four steps as follows. (1) EMD is used to decompose the dynamics of the exchange rate time series into several components of intrinsic mode function (IMF) and one residual component. (2) The IMF and residual time series phase space is reconstructed to reveal its unseen dynamics according to the optimum time delay $$\tau $$ź and embedding dimension m. (3) The reconstructed time series datasets are divided into two datasets: training and testing, in which the training datasets are used to build ELM models. (4) A regression forecast model is set up for each IMF as well as the residual component by using ELM. The final prediction results are obtained by compositing the prediction values. To verify the effectiveness of the proposed approach, four exchange rates are chosen as the forecasting targets. Compared with some existing state-of-the-art models, the proposed approach yields superior results. Academically, we demonstrated the validity and superiority of the proposed approach that integrates EMD, PSR, and ELM. Corporations or individuals can apply the results of this study to acquire accurate exchange rate information and reduce exchange rate expenses.

Financial time series forecasting has become a challenge because it is noisy, non-stationary and chaotic. To overcome this limitation, this paper uses empirical mode decomposition (EMD) to aid the financial time series forecasting and proposes an approach via combining ARIMA and SVR (Support Vector Regression) to forecast. The approach contains four steps: (1) using ARIMA to analyze the linear part of the original time series; (2) EMD is used to decompose the dynamics of the non-linear part into several intrinsic mode function (IMF) components and one residual component; (3) developing a SVR model using the above IMFs and residual components as inputs to model the nonlinear part; (4) combining the forecasting results of linear model and nonlinear model. To verify the effectiveness of the proposed approach, four stock indices are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results.

In order to extract the vibration feature of remanufactured engines, the vibration signal is decomposed by ensemble empirical mode decomposition(EEMD), and this method is applied to study vibration mode of remanufactured engines. Based on the decomposition of vibration signal, correlation coefficient is introduced to study the correlation between IMF(Intrinsic Mode Function) components and original signal, and sensitive factors of IMF components are calculated using correlation coefficients. Hilbert transform is made with sensitive IMF components. The study results show that vibration feature of remanufactured engines is include in IMF components decomposed by EEMD, the vibration modes of remanufactured engines can be differentiated by Hilbert-Huang spectrum and marginal spectrum based on sensitive IMF components. According to vibration feature, the vibration state of remanufactured engines is divided into cylinder wall, cylinder cover and crankshaft vibration modes, which are important to improve the remanufacture level of engines.

This study proposes a noise reduction technique that combines empirical mode decomposition (EMD) with wavelet thresholding to remove noise interference generated by metal slag in the damage signal of steel cord conveyor belts. The initial step involves decomposing the original signal into high-frequency and low-frequency intrinsic mode function (IMF) components using empirical mode decomposition (EMD), guided by the metal slag signal characteristics analysis. The high-frequency noise is filtered away while the low-frequency IMF components are kept intact through the wavelet threshold de-noising technique. The slag noise-containing IMF components are deleted, and the filtered signal and the low-frequency IMF components are utilized for signal reconstruction to provide the optimum noise reduction result. The study demonstrates that this method is more effective in suppressing metal slag noise, environmental noise, and other noise interferences than traditional EMD decomposition noise reduction and wavelet threshold noise reduction methods. This research provides a valuable reference for noise reduction in steel cord conveyor belt detection data.

Focusing on feature extraction of non-stationary vibration signals in condition monitoring and fault diagnosis of gear pump, the fault diagnosis approach based on empirical mode decomposition method and kernel principal component analysis (KPCA) and support vector machines (SVM) is proposed. The improved empirical mode decomposition (EMD) method is used to decompose the mechanical equipment output signal into a number of intrinsic mode function (IMF) components and a residue component, and calculated ten dimensionless parameters of each IMF and residue component, then extract result from the original parameters by using KPCA, at last the kernel principal component is classified by inputting the new feature vector to SVM for training and recognizing. The simulation and experiment results show that the advanced method is effective in restraining end effect, and the analysis result of gear pump vibration signals in different conditions validate the method is effective.

In distributed generation (DG) connected grid system, there is serious harmonic pollution caused by the wide use of inverter units and nonlinear loads. The traditional detection methods (such as Fast Fourier Transform and wavelet analysis) heavily depend on synchronous signal detection or basis functions selection, which don’t satisfy the requirements of the harmonic detection of the micro-grid environment. Therefore, a novel method combining discrete wavelet transform (DWT) with Hilbert–Huang transform (HHT) is proposed to detect and analyze the harmonic. The method uses multi resolution analysis of DWT to process the original signal. Then, a series of intrinsic mode function (IMF) components are obtained from the processed signal by using Empirical Mode Decomposition (EMD) algorithm. Thirdly, the fundamental component and harmonic are extracted from the IMF components. Finally, the frequency and amplitude of the signal are calculated by using the HHT. Simulation results demonstrate that the proposed algorithm has high precision and good real-time characteristics. Furthermore, the proposed algorithm satisfies the requirements of the harmonic detection of the DG connected grid system.

Aiming at the problem that the influence of environmental noise and insensitive damage factor make damage identification difficult and imaging error large for composite plate, a damage factor based on empirical mode decomposition(EMD) and correlation coefficient was proposed. The air-couple probe was used to obtain Lamb wave signal before and after the damage, and a group of intrinsic mode function (IMF) components of signal was obtained by EMD. According to the correlation coefficient, the IMF component which has the greatest correlation with the signal was obtained, and the relative change of its energy value was defined as the damage factor. The validity of the proposed algorithm was assessed by identifying damage and scaning imaging at composite plate. The results show that after EMD, the IMF component with the greatest correlation with the original signal is most sensitive to damage, and can be used as a damage factor to identify damage. Combining this damage factor with probability imaging algorithm for the air-coupled Lamb wave scanning can not only effectively image the defect in composite plate, but also has good noise resistance in simulated strong noise environment.

To investigate the nonstationary characteristics of strong typhoons, this paper considers the evolutionary spectral characteristics of strong typhoons based on the Hilbert-Huang transform (HHT). Discrete expressions are determined for the evolutionary spectral analysis based on the HHT. The study indicates that the classic empirical mode decomposition (EMD) method fails to extract all of the high-frequency fluctuations from wind velocity data, and the time-averaged power spectrum obtained directly using the HHT cannot provide the true wind velocity spectrum. The degrees of nonstationarity of different-order IMF components are analysed and a synthesized method of analysing the evolutionary spectrum and time-averaged power spectrum of a strong typhoon is proposed. To avoid the energy leakage problem that exists in HHT spectral analyses, the Gram-Schmidt method is used to orthogonalize the intrinsic mode function (IMF) components. The study indicates that when the orthogonalization is implemented in accordance with the sequence from high-order IMF components to low-order ones, the orthogonalized components retain the same good Hilbert property as that of the IMFs. The synthesized method proposed yields a time-averaged power spectrum that is consistent with the Fourier spectrum in value and can produce the energy distributions of a typhoon in the time and frequency domains simultaneously.

Application of the EEMD method for distinction and suppression of motion-induced noise in grounded electrical source airborne TEM system

This paper investigates the use of advance signal processing techniques for the classification of four common reciprocating compressor conditions. Non-stationary vibration signals from the compressor are decomposed based on WPT (Wavelet Packet Transform) and EMD (Empirical Mode Decomposition) and harmonic changes of the optimal components of each technique is used for condition monitoring and diagnostics of the machine. Performance comparison of WPT and EMD is done for the envelope analysis of the optimal wavelet packet and the IMF (Intrinsic Mode Function) component that can extract salient features of the four compressor conditions including one normal and three fault conditions (intercooler leakage, discharge valve leakage and the combination of intercooler and discharge valve leakage). Harmonic changes produced after envelope analysis of the best packet and IMF component are used for classification and results show that WPT technique is more robust and effective in fault classification compared to EMD.

Based on historical data, a new short-term traffic flow prediction model (ICEEMDAN-MPE-PSO-DELM-ARIMA) for intersections is proposed. In order to improve prediction accuracy, ICEEMDAN decomposition algorithm is applied on traffic flow time series to obtain multiple Intrinsic Mode Function (IMF) components. Then PSO-MPE algorithm is introduced to obtain the multi-scale permutation entropy values of each IMF component, to judge the randomness. According to the randomness, different prediction models are established. Prediction models based on PSO-DELM algorithm are established for IMF components with big randomness. ARIMA prediction models are established for IMF components with small randomness. In order to obtain the final predicted traffic flow values, multiple prediction results are added together. Finally, two actual intersections are selected to verify the proposed model. Results show that compared with other models, the proposed model has the smallest prediction errors and the best fitting effect with the real values, which can effectively improve prediction accuracy.

Ultra-short-term wind speed forecasting based on EMD-VAR model and spatial correlation

Comprehensive fatigue estimation and fault diagnosis based on Refined Generalized Multi-Scale Entropy method of centrifugal fan blades

Empirical Mode Decomposition (EMD) is a recently introduced tool for decomposing signals into so-called intrinsic mode functions (IMF). These IMF represent the data by means of oscillating waves with local zero mean. In some sense the decomposition can be compared with a time-varying filter bank, i.e., signals are decomposed using band limited filters with band widths that vary in time. The main attribute of EMD compared to other time-frequency tools is that it does not use any predetermined filters or transforms. It is therefore a self-contained method that preserves the physical properties in the separate IMF, explaining why it has been successfully applied in many engineering fields. This method is applied here on laser Doppler flowmetry signals and particularly on the hyperemia signals. Two interested hyperemia parameters are the maximum perfusion value and the corresponding time instant of appearance. Accurate values parameters are determined from the fifth IMF component. Computing these parameters allows us to improve diagnosis of some pathologies as peripheral arterial occlusive diseases.