코스피 예측을 위한 EMD를 이용한 혼합 모형

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.

The prediction of mold level is a basic and key problem of continuous casting production control. Many current techniques fail to predict the mold level because of mold level is non-linear, non-stationary and does not have a normal distribution. A hybrid model, based on empirical mode decomposition (EMD) and support vector regression (SVR), is proposed to solve the mold level in this paper. Firstly, the EMD algorithm, with adaptive decomposition, is used to decompose the original mold level signal to many intrinsic mode functions (IMFs). Then, the SVR model optimized by genetic algorithm (GA) is used to predict the IMFs and residual sequences. Finally, the equalization of the predict results is reconstructed to obtain the predict result. Several hybrid predicting methods such as EMD and autoregressive moving average model (ARMA), EMD and SVR, wavelet transform (WT) and ARMA, WT and SVR are discussed and compared in this paper. These methods are applied to mold level prediction, the experimental results show that the proposed hybrid method based on EMD and SVR is a powerful tool for solving complex time series prediction. In view of the excellent generalization ability of the EMD, it is believed that the hybrid algorithm of EMD and SVR is the best model for mold level predict among the six methods, providing a new idea for guiding continuous casting process improvement.

Using filter bank property to simplify the calculations of Empirical Mode Decomposition

Various faults inevitably occur in mechanical systems and may result in unexpected failures. Hence, fault detection is critical to reduce unscheduled downtime and costly breakdowns. Empirical mode decomposition (EMD) is an adaptive time-frequency domain signal processing method, potentially suitable for nonstationary and/or nonlinear processes. However, the EMD method suffers from several problems such as mode mixing, defined as intrinsic mode functions (IMFs) with incorrect scales. In this paper, an ensemble noise-reconstructed EMD method is proposed to ameliorate the mode mixing problem and denoise IMFs for enhancing fault signatures. The proposed method defines the IMF components as an ensemble mean of EMD trials, where each trial is obtained by sifting signals that have been reconstructed using the estimated noise present in the measured signal. Unlike traditional denoising methods, the noise inherent in the input data is reconstructed and used to reduce the background noise. Furthermore, the reconstructed noise helps to project different scales of the signal onto their corresponding IMFs, instrumental in alleviating the mode mixing problem. Two critical issues concerned in the method, i.e., the noise estimation strategy and the number of EMD trials required for denoising are discussed. Furthermore, a comprehensive noise-assisted EMD method is proposed, which includes the proposed method and ensemble EMD (EEMD). Numerical simulations and experimental case studies on accelerometer data collected from an industrial shaving process are used to demonstrate and validate the proposed method. Results show that the proposed method can both detect impending faults and isolate multiple faults. Hence, the proposed method can act as a promising tool for mechanical fault detection.

Biomedical signals are in general non-linear and non-stationary which renders them difficult to analyze with classical time series analysis techniques. Empirical Mode Decomposition (EMD) in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract informative components which are characteristic of underlying biological or physiological processes. The method is fully adaptive and generates a complete set of orthogonal basis functions, called Intrinsic Mode Functions (IMFs), in a purely data-driven manner. Amplitude and frequency of IMFs may vary over time which renders them different from conventional basis systems and ideally suited to study non-linear and non-stationary time series. However, biomedical time series are often recorded over long time periods. This generates the need for efficient EMD algorithms which can analyze the data in real time. No such algorithms yet exist which are robust, efficient and easy to implement. The contribution shortly reviews the technique of EMD and related algorithms and develops an on-line variant, called slidingEMD, which is shown to perform well on large scale biomedical time series recorded during neuromonitoring.

In this study, an empirical mode decomposition (EMD) based least square support vector machine (LSSVM) ensemble adaptive learning paradigm is proposed for time series forecasting. For this purpose, the original time series are first decomposed into several intrinsic mode functions (IMFs) and one residual component. Then phase space reconstruction (PSR) is done in each component, where the samples are put into LSSVM for training. Particle swarm optimization algorithm (PSO) is used to achieve the adaptive optimization of forecasting models in different components, which makes LSSVMs better describe the signal characteristics in different scales, thus greatly improves the efficiency and accuracy of the learning and training; Finally, the forecasting values of the original series are obtained through the reconstruction of the forecasting values in each component. For illustration and verification, the spare parts cost series for a vessel is used to test the effectiveness of the proposed EMD-LSSVM-PSO ensemble adaptive learning methodology. Empirical results obtained demonstrate the attractiveness of the proposed method.

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.

As a trading strategy, pairs trading is performed based on the arbitrage opportunities extracted from statistical models. It is an outcome of the distance between an asset pair and the equilibrium state. Consequently, selecting a pair with the potential to form long-term relationships and reverting to the mean is the main challenge associated with pair trading. Cointegration is one of the most famous statistical tests for selecting a pair's trading. The present study uses Empirical Mode Decomposition (EMD) to decompose the time series of an asset pair price into its constituent elements (intrinsic mode functions). This study examined the property of cointegration across different levels and the corresponding levels of 2- time series to find the cointegration pairs in different decomposition levels and finally examine the resulting profitability. To this end, the profitability of the pairs trading system related to 14 stocks of the Tehran Stock Exchange throughout 2012-2021 was investigated based on EMD. The results showed that the outputs are pretty noticeable for the first level of decomposition (the first intrinsic mode function), and the number of trading opportunities increased by more than two times compared to the normal pair trading with cointegration; the daily returns increased by four times; and the Sharpe ratio increased by about two times compared to the normal pairs trading. The system formed based on the second mode function also outperformed the normal cointegration, and the performance of the third intrinsic mode function is almost on par with that of cointegration. Moreover, the mean transaction duration decreased remarkably in the first and second mode functions.

Accurate forecast of container throughput is an important basis for reasonably planning port construction, making port operation plan and adjusting port development direction. Aiming at the complex nonlinear characteristics of port container throughput, a hybrid model based on selective deep-ensemble for container throughput forecasting (HMSD) is presented in this paper. Firstly, this model decomposes the original container throughput time series into several intrinsic mode functions and a residual by empirical mode decomposition (EMD). Considering highly nonlinear characteristics of each intrinsic mode function, the proposed model constructs three deep neural networks, namely, long short term memory (LSTM), gated recurrent unit (GRU) and convolutional neural network (CNN), as base learners to predict intrinsic mode functions. Then, this model establishes selective deep-ensemble forecasting model by improved group method of data handling (GMDH) on intrinsic mode functions and obtains their ensemble forecasting results. Furthermore, this model uses an autoregressive integrated moving average model to predict the linear residual. Finally, the forecasting result of the total container throughput is obtained by integrating the forecasting results of all intrinsic mode functions and the residual. The empirical analysis of container throughput data of Xiamen port and Shanghai port in China shows that the HMSD model has better forecasting effect than other models.

Time series forecasting can be viewed as an indispensable topic under discussion in a wide range of fields including, medicine, engineering, finance and economics, etc. As assumed by the conventional time series models static environmental conditions are rare to exist in real life situations. This study addressed this issue in case of financial markets by proposing an intelligent decision support system for Forex (Foreign Exchange) trading which can be used even under dynamic environmental conditions. The study proposes to identify clusters of time points relevant to dynamic environments defined by the release of scheduled news items. Then the proposed system utilizes the relevant database of time points to derive the forecast. The proposed intelligent system is supported with a hybrid neural network approach which combines generalized autoregressive conditional heteroskedasticity (GARCH) volatility estimates and intrinsic mode functions derived from Empirical Mode Decomposition. The incorporation of clustering of time points found significance in the forecasting process utilized in the proposed intelligent system over the method which ignores clustering. Moreover, the proposed system is efficient enough in delivering the trading decision yielding instant profitable opportunities, 57% of times and aggregated profit over hundred trading positions remained positive at almost all the time points.

The key to the fault diagnosis of rotating machinery is to extract the fault characteristics from the vibration signal of rotating machinery. Signal analysis and processing are the most commonly used methods for feature extraction. In this paper, a method combining the empirical mode decomposition (EMD) and spectral kurtosis is presented for fault detection of rotating machinery based on vibrational signal. According to the modulation characteristic of the rolling element bearing fault signal and the disadvantage of depending on the experience to select resonance high frequency band, an improved method integrating EMD with spectrum kurtosis for rolling bearing fault diagnosis is put forward. Firstly, the bearing fault signal is decomposed into a series of intrinsic mode function (IMF) through EMD method. Secondly, the false IMF components are eliminated through mutual information, kurtosis and cross-correlation, then the useful IMFs are selected to construct the fault signal. Finally, by designing the optimal band pass filter with the spectral kurtosis, it can be obtained that, the envelope demodulation spectrum of the filtered signal, and the fault feature of rolling bearing are extracted. The experimental result of the rolling bearing fault diagnosis shows that the proposed method can effectively extract the weak fault feature of rolling bearing, which is superior to envelope analysis.

In this paper, a new perceptual spread spectrum audio watermarking scheme is discussed. The watermark embedding process is performed in the Empirical Mode Decomposition (EMD) domain, and the hybrid watermark extraction process is based on the combination of EMD and ISA (Independent Subspace Analysis) techniques, followed by the generic detection system, i.e. inverse perceptual filter, predictor filter and correlation based detector. Since the EMD decomposes the audio signal into several oscillating components---the intrinsic mode functions (IMF)---the watermark information can be inserted in more than one IMF, using spread spectrum modulation, allowing hence the increase of the insertion capacity. The imperceptibility of the inserted data is ensured by the use of a psychoacoustical model. The blind extraction of the watermark signal, from the received watermarked audio, consists in the separation of the watermark from the IMFs of the received audio signal. The separation is achieved by a new proposed under-determined ISA method, here referred to as UISA. The proposed hybrid watermarking system was applied to the SQAM (Sound Quality Assessment Material) audio database (Available at http://sound.media.mit.edu/mpeg4/audio/sqam/ ) and proved to have efficient detection performances in terms of Bit Error Rate (BER) compared to a generic perceptual spread spectrum watermarking system. The perceptual quality of the watermarked audio was objectively assessed using the PEMO-Q (Tool for objective perceptual assessment of audio quality) algorithm. Also, using our technique, we can extract the different watermarks without using any information of original signal or the inserted watermark. Experimental results exhibit that the transparency and high robustness of the watermarked audio can be achieved simultaneously with a substantial increase of the amount of information transmitted. A reliability of 1.8 10Â?Â?Â?4 (against 1.5 10Â?Â?Â?2 for the generic system), for a bit rate of 400 bits/s, can be achieved when the channel is not disturbed.

Sleep disorders has a vital effect on mental depression and many other diseases of human body. Diagnosing the sleep disorder in an early curable stage may help to provide better treatment and save the life. The EEG (Electroencephalogram) signal is one of the most uses bio signal for capturing brain activities to detect and diagnosis the sleep disorders. Empirical mode decomposition (EMD) is an efficient time-frequency data analysis technique for diagnosing disease by analyzing EEG signal. However, it is a challenging issue to select the optimal intrinsic mode functions (IMFs) of Empirical mode decomposition (EMD) for extracting discriminant properties of EEG signals to diagnosis the sleep disorder. From this point of view, this paper presents a model to select optimal IMF of EMD for diagnosing the sleep disorder using EEG brain signal. In this proposed model, EMD is applied to decompose and analyze EEG signal for extracting biomarker/feature of sleep disorders. During the EMD decomposition process, different levels of IMF are extracted and features, i.e., Shannon Entropy, Spectral Entropy, Standard deviation, Skewness and Kurtosis are calculated from those IMFs for detecting the sleep disorders. In identification process, the multiclass support vector machine (MC-SVM) classification algorithm is used and sleep disorders are classified based on trained knowledge. Finally, the performance of proposed model is evaluated for different IMFs of EMD and find the optimal IMF for sleep disorder diagnosis. For evaluating the proposed model, a benchmark dataset including 4 types of data such as Apnea, REM, PLM and healthy subjects are used in experiment. According to the experimental result, the proposed model achieves the optimal classification performance for IMF 8, i.e., 93.24% average classification accuracy.

Identification of dominant periodicities is a very important but difficult task in astronomical time series analysis. In the present paper, a new method of periodic identification is proposed in which empirical mode decomposition (EMD) and wavelet transform analysis (WTA) are used in combination. We firstly apply EMD method to decompose a time series into several intrinsic mode functions (IMFs), and then by using WTA approach to identify periodicities in each of IMFs, and finally all of the actual periodicites in astronomical time series can be obtained. Analyses of an observational data set indicate better performance of the proposed EMD-WTA method to identify periodicities. Compared with date compensated discrete Fourier transform and Lomb-Scargle period gram methods which are widely used presently, the EMD-WTA method not only can improve the periodic identifying capability of a time series, but also can improve overall periodic identification by being able to distinguish system noise, quasi-periodicities, and secular trend.

A filtering method based on empirical mode decomposition (EMD) for coulostatically-induced transients (CITs) was presented. The effect of EMD filtering was evaluated from different simulation systems. To boost the applicable solution for EMD filtering and reduce the subjectivity of filtering, discussions of the simulating noises were mainly focused on white noise, high frequency noise, 50 Hz noise from commercial power and blend of above-mentioned noises. To illustrate the most importance of EMD filtering for the goodness of fit of CIS, one real corrosion system involving multi-time constant was simultaneously investigated by electrochemical impedance spectrum (EIS) and coulostatically-induced spectrum (CIS). The results reported here demonstrate that EMD de-noising is applicable for CITs. The proposed method shows good performance of de-noising and is an alternative de-noising method for the CITs analysis. CIS with EMD filtering are in good agreement with EIS. For practical application of EMD, removal of the first two items of intrinsic mode function (IMF) and 50 Hz element in forth IMF will be generally satisfactory for CIS analysis.