scholarly journals Multiscale Combined Model Based on Run-Length-Judgment Method and Its Application in Oil Price Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Wang Shu-ping ◽  
Hu Ai-mei ◽  
Wu Zhen-xin ◽  
Liu Ya-qing ◽  
Bai Xiao-wei
Keyword(s):  
Low Frequency ◽  
Oil Price ◽  
Support Vector ◽  
Combined Model ◽  
Run Length ◽  
Price Series ◽  
Mode Decomposition ◽  
Combined Models ◽  
Artificial Neural Network Ann ◽  
Combined Forecasting

Forecasting of oil price is an important area of energy market research. Based on the idea of decomposition-reconstruction-integration, this paper built a new multiscale combined forecasting model with the methods of empirical mode decomposition (EMD), artificial neural network (ANN), support vector machine (SVM), and time series methods. While building the model, we proposed a new idea to use run length judgment method to reconstruct the component sequences. Then this model was applied to analyze the fluctuation and trend of international oil price. Oil price series was decomposed and reconstructed into high frequency, medium frequency, low frequency, and trend sequences. Different features of fluctuation can be explained by irregular factors, season factors, major events, and long-term trend. Empirical analysis showed that the multiscale combined model obtained the best forecasting result compared with single models including ARIMA, Elman, SVM, and GARCH and combined models including ARIMA-SVM model and EMD-SVM-SVM method.

Data Mining Methods for Crude Oil Market Analysis and Forecast

2010 ◽  
pp. 184-203 ◽  
Author(s):  
Jue Wang ◽  
Wei Xu ◽  
Xun Zhang ◽  
Yejing Bao ◽  
Ye Pang ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Crude Oil ◽  
Nonlinear Models ◽  
Oil Price ◽  
Support Vector ◽  
Public And Private ◽  
Governmental Agencies ◽  
Artificial Neural Network Ann ◽  
Price Analysis

In this study, two data mining based models are proposed for crude oil price analysis and forecasting, one of which is a hybrid wavelet decomposition and support vector Machine (SVM) model and the other is an OECD petroleum inventory levels based wavelet neural network model (WNN). These models utilize support vector regression (SVR) and artificial neural network (ANN) technique for crude oil prediction and are made comparison with other forecasting models, respectively. Empirical results show that the proposed nonlinear models can improve the performance of oil price forecasting. The findings of this research are useful for private organizations and governmental agencies to take either preventive or corrective actions to reduce the impact of large fluctuation in crude oil markets, and demonstrate that the implications of data mining in public and private sectors and government agencies are promising for analyzing and predicting on the basis of data.

scholarly journals Two-Stage Hybrid Machine Learning Model for High-Frequency Intraday Bitcoin Price Prediction Based on Technical Indicators, Variational Mode Decomposition, and Support Vector Regression

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Samuel Asante Gyamerah
Keyword(s):  
Support Vector Regression ◽  
Technical Analysis ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Two Stage ◽  
Technical Indicators ◽  
Price Prediction ◽  
Price Series ◽  
Mode Decomposition ◽  
Statistical Trends

Due to the inherent chaotic and fractal dynamics in the price series of Bitcoin, this paper proposes a two-stage Bitcoin price prediction model by combining the advantage of variational mode decomposition (VMD) and technical analysis. VMD eliminates the noise signals and stochastic volatility in the price data by decomposing the data into variational mode functions, while technical analysis uses statistical trends obtained from past trading activity and price changes to construct technical indicators. The support vector regression (SVR) accepts input from a hybrid of technical indicators (TI) and reconstructed variational mode functions (rVMF). The model is trained, validated, and tested in a period characterized by unprecedented economic turmoil due to the COVID-19 pandemic, allowing the evaluation of the model in the presence of the pandemic. The constructed hybrid model outperforms the single SVR model that uses only TI and rVMF as features. The ability to predict a minute intraday Bitcoin price has a huge propensity to reduce investors’ exposure to risk and provides better assurances of annualized returns.

Multi-Scale Volatility Feature Analysis and Prediction of Gold Price

2017 ◽  
Vol 16 (01) ◽  
pp. 205-223 ◽  
Author(s):  
Fenghua Wen ◽  
Xin Yang ◽  
Xu Gong ◽  
Kin Keung Lai
Keyword(s):  
Structural Breaks ◽  
Price Volatility ◽  
Ensemble Empirical Mode Decomposition ◽  
Gold Price ◽  
Support Vector ◽  
Multi Scale ◽  
Mode Decomposition ◽  
One Step ◽  
External Events ◽  
Artificial Neural Network Ann

Volatility of gold price is of great significance for avoiding the risk of gold investment. It is necessary to understand the effect of external events and intrinsic regularities to make accurate price predictions. This paper first compared EMD with CEEMD algorithm, and the results find that CEEMD algorithm performance is better than that of EMD in analysis gold price volatility. Then this paper uses the complementary ensemble empirical mode decomposition (CEEMD) to decompose the historical price of international gold into price components at different frequencies, and extracts a short-term fluctuation, a shock from significant events and a long-term price. In addition, this paper combines the Iterative cumulative sum of squares (ICSS) with Chow test to test the three event prices for structural breaks, and analyzes the effect of external events on volatility of gold price by comparing the external events with the test results for structural breaks. Finally, this paper constructs support vector machine (SVM) models and artificial neural network (ANN) on three series for prediction, and finds that the SVM performed better in gold price prediction in one-step-ahead and five-step-ahead, and when we combine the SVMs and ANNs with price components to make predictions, the error of the combined prediction is smaller than SVMs and ANNs with separate terms of series extracted.

scholarly journals A New Hybrid Model for Underwater Acoustic Signal Prediction

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Guohui Li ◽  
Wanni Chang ◽  
Hong Yang
Keyword(s):  
Hybrid Model ◽  
Acoustic Signal ◽  
Signal Sequence ◽  
Model Performance ◽  
Low Frequency ◽  
Support Vector ◽  
Underwater Acoustic ◽  
Bee Colony ◽  
Mode Decomposition ◽  
Underwater Acoustic Signal

The prediction of underwater acoustic signal is the basis of underwater acoustic signal processing, which can be applied to underwater target signal noise reduction, detection, and feature extraction. Therefore, it is of great significance to improve the prediction accuracy of underwater acoustic signal. Aiming at the difficulty in underwater acoustic signal sequence prediction, a new hybrid prediction model for underwater acoustic signal is proposed in this paper, which combines the advantages of variational mode decomposition (VMD), artificial intelligence method, and optimization algorithm. In order to reduce the complexity of underwater acoustic signal sequence and improve operation efficiency, the original signal is decomposed by VMD into intrinsic mode components (IMFs) according to the characteristics of the signal, and dispersion entropy (DE) is used to analyze the complexity of IMF. The subsequences (VMD-DE) are obtained by adding the IMF with similar complexity. Then, extreme learning machine (ELM) is used to predict the low-frequency subsequence obtained by VMD-DE. Support vector regression (SVR) is used to predict the high-frequency subsequence. In addition, an artificial bee colony (ABC) algorithm is used to optimize model performance by adjusting the parameters of SVR. The experimental results show that the proposed new hybrid model can provide enhanced accuracy with the reduction of prediction error compared with other existing prediction methods.

scholarly journals A combined model for short-term wind speed forecasting based on empirical mode decomposition, feature selection, support vector regression and cross-validated lasso

2021 ◽  
Vol 7 ◽  
pp. e732
Author(s):  
Tao Wang
Keyword(s):  
Time Series ◽  
Feature Selection ◽  
Wind Speed ◽  
Support Vector Regression ◽  
Prediction Performance ◽  
Support Vector ◽  
Short Term ◽  
Combined Model ◽  
Wind Speed Forecasting ◽  
Mode Decomposition

Background The planning and control of wind power production rely heavily on short-term wind speed forecasting. Due to the non-linearity and non-stationarity of wind, it is difficult to carry out accurate modeling and prediction through traditional wind speed forecasting models. Methods In the paper, we combine empirical mode decomposition (EMD), feature selection (FS), support vector regression (SVR) and cross-validated lasso (LassoCV) to develop a new wind speed forecasting model, aiming to improve the prediction performance of wind speed. EMD is used to extract the intrinsic mode functions (IMFs) from the original wind speed time series to eliminate the non-stationarity in the time series. FS and SVR are combined to predict the high-frequency IMF obtained by EMD. LassoCV is used to complete the prediction of low-frequency IMF and trend. Results Data collected from two wind stations in Michigan, USA are adopted to test the proposed combined model. Experimental results show that in multi-step wind speed forecasting, compared with the classic individual and traditional EMD-based combined models, the proposed model has better prediction performance. Conclusions Through the proposed combined model, the wind speed forecast can be effectively improved.

Crude oil price prediction based on LSTM network and GM (1,1) model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tianxiang Yao ◽  
Zihan Wang
Keyword(s):  
Crude Oil ◽  
Oil Price ◽  
Crude Oil Price ◽  
Short Term ◽  
Content Type ◽  
Forecasting Models ◽  
Price Series ◽  
Mode Decomposition ◽  
Lstm Network

PurposeAccording to the problem of crude oil price forecasting, the purpose of this paper is to propose a multi-step prediction method based on the empirical mode decomposition, long short-term memory network and GM (1,1) model.Design/methodology/approachFirst, the empirical mode decomposition method is used to decompose the crude oil price series into several components with different frequencies. Then, each subsequence is classified and synthesized based on the specific periodicity and other properties to obtain several components with different significant characteristics. Finally, all components are substituted into a suitable prediction model for fitting. LSTM models with different parameters are constructed for predicting specific components, which approximately and respectively represent short-term market disturbance and long-term influences. Rolling GM (1,1) model is constructed to simulate a series representing the development trend of oil price. Eventually, all results obtained from forecasting models are summarized to evaluate the performance of the model.FindingsThe model is respectively applied to simulate daily, weekly and monthly WTI crude oil price sequences. The results show that the model has high accuracy on the prediction, especially in terms of series representing long-term influences with lower frequency. GM (1,1) model has excellent performance on fitting the trend of crude oil price.Originality/valueThis paper combines GM (1,1) model with LSTM network to forecast WTI crude oil price series. According to the different characteristics of different sequences, suitable forecasting models are constructed to simulate the components.

scholarly journals Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory

Mathematics ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 1188 ◽  
Author(s):  
Yuanyuan Zhou ◽  
Min Zhou ◽  
Qing Xia ◽  
Wei-Chiang Hong
Keyword(s):  
Energy Savings ◽  
Electricity Consumption ◽  
Low Frequency ◽  
Support Vector ◽  
Forecasting Model ◽  
Management Approach ◽  
Intrinsic Mode Functions ◽  
Residual Term ◽  
Quantum Genetic Algorithm ◽  
Mode Decomposition

In the context of the nationwide call for “energy savings” in China, it is desirable to establish a more accurate forecasting model to manage the electricity consumption from the university dormitory, to provide a suitable management approach, and eventually, to achieve the “green campus” policy. This paper applies the empirical mode decomposition (EMD) method and the quantum genetic algorithm (QGA) hybridizing with the support vector regression (SVR) model to forecast the daily electricity consumption. Among the decomposed intrinsic mode functions (IMFs), define three meaningful items: item A contains the terms but the residual term; item B contains the terms but without the top two IMFs (with high randomness); and item C contains the terms without the first two IMFs and the residual term, where the first two terms imply the first two high-frequency part of the electricity consumption data, and the residual term is the low-frequency part. These three items are separately modeled by the employed SVR-QGA model, and the final forecasting values would be computed as A + B − C. Therefore, this paper proposes an effective electricity consumption forecasting model, namely EMD-SVR-QGA model, with these three items to forecast the electricity consumption of a university dormitory, China. The forecasting results indicate that the proposed model outperforms other compared models.

scholarly journals Wavelet-support vector machine for forecasting palm oil price

2019 ◽  
Vol 15 (3) ◽  
pp. 398-406
Author(s):  
Ani Shabri ◽  
Mohd Fahmi Abdul Hamid
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Palm Oil ◽  
Oil Price ◽  
Support Vector ◽  
Data Series ◽  
Discrete Wavelet ◽  
Ann Model ◽  
Svm Model ◽  
Artificial Neural Network Ann

This study examines the feasibility of applying Wavelet-Support Vector Machine (W-SVM) model in forecasting palm oil price. The conjunction method wavelet-support vector machine (W-SVM) is obtained by the integration of discrete wavelet transform (DWT) method and support vector machine (SVM). In W-SVM model, wavelet transform is used to decompose data series into two parts; approximation series and details series. This decomposed series were then used as the input to the SVM model to forecast the palm oil price. This study also utilizes the application of partial correlation-based input variable selection as the preprocessing steps in determining the best input to the model. The performance of the W-SVM model was then compared with the classical SVM model and also artificial neural network (ANN) model. The empirical result shows that the addition of wavelet technique in W-SVM model enhances the forecasting performance of classical SVM and performs better than ANN.

Study on Low Frequency Oscillation of Wind Power System Based on HHT

2014 ◽  
Vol 981 ◽  
pp. 663-667
Author(s):  
Hong Ling Xie ◽  
Ting Yue
Keyword(s):  
Power System ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Support Vector ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
B Spline ◽  
Mode Decomposition ◽  
Wind Power System

For the output of wind power system has the characteristics of randomness, volatility and intermittence, the voltage of wind power system low frequency oscillation is one of the most common fluctuations in the system. For the problem of low frequency oscillation, the limitations of the detection methods such as the Lyapunov linearization method, the Prony method, wavelet transform method are summed up, and a new detecting method named Hilbert-huang Transform (HHT) is put forward in this paper, which can detect the oscillation accurately and timely. To solve the problem of end effect in the process of empirical mode decomposition (EMD), B-spline empirical mode decomposition based on support vector machine is applied in dealing with the end issue. an extension of the original signal is applied. Then, calculating the average curve of the signal by B-spline interpolation method. Finally getting the intrinsic mode function (IMF) by empirical mode decomposition (EMD). The practicality of the method is verified by Matlab simulation.

ENERGY FEATURE EXTRACTION AND SVM CLASSIFICATION OF MOTOR IMAGERY-INDUCED ELECTROENCEPHALOGRAMS

2012 ◽  
Vol 05 (02) ◽  
pp. 1250006 ◽  
Author(s):  
JIANING ZHENG ◽  
LIYU HUANG ◽  
JING ZHAO
Keyword(s):  
Motor Imagery ◽  
Low Frequency ◽  
New Combination ◽  
Support Vector ◽  
Frequency Noise ◽  
Intrinsic Mode Functions ◽  
Identification Rate ◽  
Mode Decomposition ◽  
Eeg Data ◽  
Optimal Kernel

The precise classification for the electroencephalogram (EEG) in different mental tasks in the research on brain–computer interface (BCI) is the key for the design and clinical application of the system. In this paper, a new combination classification algorithm is presented and tested using the EEG data of right and left motor imagery experiments. First, to eliminate the low frequency noise in the original EEGs, the signals were decomposed by empirical mode decomposition (EMD) and then the optimal kernel parameters for support vector machine (SVM) were determined, the energy features of the first three intrinsic mode functions (IMFs) of every signal were extracted and used as input vectors of the employed SVM. The output of the SVM will be classification result for different mental task EEG signals. The study shows that mean identification rate of the proposed algorithm is 95%, which is much better than the present traditional algorithms.

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