scholarly journals Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine

2021 ◽  
Author(s):  
Peng Chen ◽  
Andrew Vivian ◽  
Cheng Ye
Keyword(s):  
Extreme Learning Machine ◽  
Low Frequency ◽  
Reconstruction Algorithm ◽  
Fuzzy Entropy ◽  
Futures Prices ◽  
Clustering Methods ◽  
Intrinsic Mode Functions ◽  
Forecasting Models ◽  
Hybrid Forecasting ◽  
Learning Machine

AbstractIn this paper, we propose a novel hybrid model that extends prior work involving ensemble empirical mode decomposition (EEMD) by using fuzzy entropy and extreme learning machine (ELM) methods. We demonstrate this 3-stage model by applying it to forecast carbon futures prices which are characterized by chaos and complexity. First, we employ the EEMD method to decompose carbon futures prices into a couple of intrinsic mode functions (IMFs) and one residue. Second, the fuzzy entropy and K-means clustering methods are used to reconstruct the IMFs and the residue to obtain three reconstructed components, specifically a high frequency series, a low frequency series, and a trend series. Third, the ARMA model is implemented for the stationary high and low frequency series, while the extreme learning machine (ELM) model is utilized for the non-stationary trend series. Finally, all the component forecasts are aggregated to form final forecasts of the carbon price for each model. The empirical results show that the proposed reconstruction algorithm can bring more than 40% improvement in prediction accuracy compared to the traditional fine-to-coarse reconstruction algorithm under the same forecasting framework. The hybrid forecasting model proposed in this paper also well captures the direction of the price changes, with strong and robust forecasting ability, which is significantly better than the single forecasting models and the other hybrid forecasting models.

scholarly journals Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Qisheng Yan ◽  
Shitong Wang ◽  
Bingqing Li
Keyword(s):  
Phase Space ◽  
Extreme Learning Machine ◽  
Empirical Mode Decomposition ◽  
Rbf Neural Network ◽  
Phase Space Reconstruction ◽  
Intrinsic Mode Functions ◽  
Price Series ◽  
Mode Decomposition ◽  
Hybrid Forecasting ◽  
Learning Machine

A hybrid forecasting approach combining empirical mode decomposition (EMD), phase space reconstruction (PSR), and extreme learning machine (ELM) for international uranium resource prices is proposed. In the first stage, the original uranium resource price series are first decomposed into a finite number of independent intrinsic mode functions (IMFs), with different frequencies. In the second stage, the IMFs are composed into three subseries based on the fine-to-coarse reconstruction rule. In the third stage, based on phase space reconstruction, different ELM models are used to model and forecast the three subseries, respectively, according to the intrinsic characteristic time scales. Finally, in the foruth stage, these forecasting results are combined to output the ultimate forecasting result. Experimental results from real uranium resource price data demonstrate that the proposed hybrid forecasting method outperforms RBF neural network (RBFNN) and single ELM in terms of RMSE, MAE, and DS.

scholarly journals Forecasting Computer Products Sales by Integrating Ensemble Empirical Mode Decomposition and Extreme Learning Machine

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Chi-Jie Lu ◽  
Yuehjen E. Shao
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Original Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Local Characteristic ◽  
Forecasting Model ◽  
Intrinsic Mode Functions ◽  
Model Decomposition ◽  
Sales Data ◽  
Learning Machine

A hybrid forecasting model that integrates ensemble empirical model decomposition (EEMD), and extreme learning machine (ELM) for computer products sales is proposed. The EEMD is a new piece of signal processing technology. It is based on the local characteristic time scales of a signal and could decompose the complicated signal into intrinsic mode functions (IMFs). The ELM is a novel learning algorithm for single-hidden-layer feedforward networks. In our proposed approach, the initial task is to apply the EEMD method to decompose the original sales data into a number of IMFs. The hidden useful information of the original data could be discovered in those IMFs. The IMFs are then integrated with the ELM method to develop an effective forecasting model for computer products sales. Experimental results from three real computer products sales data, including hard disk, display card, and notebook, showed that the proposed hybrid sales forecasting method outperforms the four comparative models and is an effective alternative for forecasting sales of computer products.

scholarly journals Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Lili Chen ◽  
Yaru Hao
Keyword(s):  
Feature Extraction ◽  
Analytic Function ◽  
Extreme Learning Machine ◽  
Maximum Amplitude ◽  
Experimental Results ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Learning Machine ◽  
Labour Group

Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM). For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC) curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.

scholarly journals A Framework for Final Drive Simultaneous Failure Diagnosis Based on Fuzzy Entropy and Sparse Bayesian Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Qing Ye ◽  
Hao Pan ◽  
Changhua Liu
Keyword(s):  
Feature Extraction ◽  
Extreme Learning Machine ◽  
Failure Modes ◽  
Fuzzy Entropy ◽  
Failure Diagnosis ◽  
Optimal Decision ◽  
Support Vector ◽  
Decision Threshold ◽  
Learning Machine ◽  
Final Drive

This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based onF1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach.

scholarly journals An Application of Hybrid Forecasting Singular Spectrum Analysis – Extreme Learning Machine Method in Foreign Tourists Forecasting

2019 ◽  
Vol 5 (2) ◽  
pp. 60-68
Author(s):  
Muhammad Fajar
Keyword(s):  
Extreme Learning Machine ◽  
Spectrum Analysis ◽  
Input Parameter ◽  
Singular Spectrum Analysis ◽  
Tourism Development ◽  
Machine Method ◽  
Singular Spectrum ◽  
Business Opportunities ◽  
Hybrid Forecasting ◽  
Learning Machine

International tourism is one indicator of measuring tourism development. Tourism development is important for the national economy since tourism could boost foreign exchange, create business opportunities, and provide employment opportunities. The prediction of foreign tourist numbers in the future obtained from forecasting is used as an input parameter for strategy and tourism programs planning. In this paper, the Hybrid Singular Spectrum Analysis – Extreme Learning Machine (SSA-ELM) is used to forecast the number of foreign tourists.  Data used is the number of foreign tourists January 1980 - December 2017 taken from Badan Pusat Statistik (Statistics Indonesia). The result of this research concludes that Hybrid SSA-ELM performance is very good at forecasting the number of foreign tourists. It is shown by the MAPE value of 4.91 percent with eight observations out a sample.

scholarly journals Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 950 ◽  
Author(s):  
Jianguo Zhou ◽  
Xuejing Huo ◽  
Xiaolei Xu ◽  
Yushuo Li
Keyword(s):  
Extreme Point ◽  
Extreme Learning Machine ◽  
Carbon Price ◽  
Grey Wolf Optimizer ◽  
Symmetric Mode ◽  
Grey Wolf ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Learning Machine

Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-point symmetric mode decomposition, an extreme learning machine, and a grey wolf optimizer algorithm. Firstly, the extreme-point symmetric mode decomposition is employed to decompose the carbon price into several intrinsic mode functions and one residue. Then, the partial autocorrelation function is utilized to determine the input variables of the intrinsic mode functions, and the residue of the extreme learning machine. In the end, the grey wolf optimizer algorithm is applied to optimize the extreme learning machine, to forecast the carbon price. To illustrate the superiority of the proposed model, the Hubei, Beijing, Shanghai, and Guangdong carbon price series are selected for the predictions. The empirical results confirm that the proposed model is superior to the other benchmark methods. Consequently, the proposed model can be employed as an effective method for carbon price series analysis and forecasting.

scholarly journals Improved Butterfly Optimizer-Configured Extreme Learning Machine for Fault Diagnosis

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Helong Yu ◽  
Kang Yuan ◽  
Wenshu Li ◽  
Nannan Zhao ◽  
Weibin Chen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Rolling Bearing ◽  
Classification Performance ◽  
Feature Subset ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Outer Race ◽  
Rolling Ball ◽  
Learning Machine

An efficient intelligent fault diagnosis model was proposed in this paper to timely and accurately offer a dependable basis for identifying the rolling bearing condition in the actual production application. The model is mainly based on an improved butterfly optimizer algorithm- (BOA-) optimized kernel extreme learning machine (KELM) model. Firstly, the roller bearing’s vibration signals in the four states that contain normal state, outer race failure, inner race failure, and rolling ball failure are decomposed into several intrinsic mode functions (IMFs) using the complete ensemble empirical mode decomposition based on adaptive noise (CEEMDAN). Then, the amplitude energy entropies of IMFs are designated as the features of the rolling bearing. In order to eliminate redundant features, a random forest was used to receive the contributions of features to the accuracy of results, and subsets of features were set up by removing one feature in the descending order, using the classification accuracy of the SBOA-KELM model as the criterion to obtain the optimal feature subset. The salp swarm algorithm (SSA) was introduced to BOA to improve optimization ability, obtain optimal KELM parameters, and avoid the BOA deteriorating into local optimization. Finally, an optimal SBOA-KELM model was constructed for the identification of rolling bearings. In the experiment, SBOA was validated against ten other competitive optimization algorithms on 30 IEEE CEC2017 benchmark functions. The experimental results validated that the SBOA was evident over existing algorithms for most function problems. SBOA-KELM employed for diagnosing the fault diagnosis of rolling bearings obtained improved classification performance and higher stability. Therefore, the proposed SBOA-KELM model can be effectively used to diagnose faults of rolling bearings.

scholarly journals Degradation Tendency Measurement of Aircraft Engines Based on FEEMD Permutation Entropy and Regularized Extreme Learning Machine Using Multi-Sensor Data

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3301
Author(s):  
Wei Jiang ◽  
Yanhe Xu ◽  
Yahui Shan ◽  
Han Liu
Keyword(s):  
Extreme Learning Machine ◽  
Aircraft Engine ◽  
Sensor Data ◽  
Permutation Entropy ◽  
Aircraft Engines ◽  
Intrinsic Mode Functions ◽  
Degradation Index ◽  
Trend Prediction ◽  
Learning Machine ◽  
Prediction Approach

As the core component and main power source for aircrafts, the reliability of an aero engine is vital for the security operation of aircrafts. Degradation tendency measurement on an engine can not only improve its safety, but effectively reduce the maintenance costs. In this paper, a hybrid method using multi-sensor data based on fast ensemble empirical mode decomposition permutation entropy (FEEMD-PE) and regularized extreme learning machine (RELM), systematically blending the signal processing technology and trend prediction approach, is proposed for aircraft engine degradation tendency measurement. Firstly, a synthesized degradation index was designed utilizing multi-sensor data and a data fusion technique to evaluate the degradation level of the engine unit. Secondly, in order to eliminate the irregular data fluctuation, FEEMD was employed to efficiently decompose the constructed degradation index series. Subsequently, considering the complexity of intrinsic mode functions (IMFs) obtained through sequence decomposition, a permutation entropy-based reconstruction strategy was innovatively developed to generate the refactored IMFs (RIMFs), which have stronger ability for describing the degradation states and contribute to improving the prediction accuracy. Finally, RIMFs were used as the inputs of the RELM model to measure the degradation tendency. The proposed method was applied to the degradation tendency measurement of aircraft engines. The results confirm the effectiveness and superiority of the proposed method, and it is more suitable for actual applications compared with other existing approaches.

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