scholarly journals Effective forecasting of stock market price by using extreme learning machine optimized by PSO-based group oriented crow search algorithm

2021 ◽  
Author(s):  
Sudeepa Das ◽  
Tirath Prasad Sahu ◽  
Rekh Ram Janghel ◽  
Binod Kumar Sahu
Keyword(s):  
Stock Market ◽  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Market Price ◽  
Learning Machine

scholarly journals Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 4896
Author(s):  
Jianguo Zhou ◽  
Dongfeng Chen
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Emission Reduction ◽  
Search Algorithm ◽  
Carbon Price ◽  
Integration Scheme ◽  
Combination Model ◽  
Price Prediction ◽  
Price Series ◽  
Learning Machine

Effective carbon pricing policies have become an effective tool for many countries to encourage emission reduction. An accurate carbon price prediction model is helpful for the implementation of energy conservation and emission reduction policies and the decision-making of governments and investors. However, it is difficult for a single prediction model to achieve high prediction accuracy because of the high complexity of the carbon price series. Many studies have proved the nonlinear characteristics of carbon trading prices, but there are very few studies on the chaotic nature of carbon price series. As a consequence, this paper proposes an innovative hybrid model for carbon price prediction. A decomposition-reconstruction-prediction-integration scheme is designed to predict carbon prices. Firstly, several intrinsic mode functions (IMFs) and one residue were obtained from the raw data decomposed by ICEEMDAN. Next, the decomposed subsection is reconstructed into a new sequence according to the calculation results by the Lempel-Ziv complexity algorithm. Then, considering the chaotic characteristics of sequence, the input variables of the models are determined through the phase space reconstruction (PSR) algorithm combined with the partial autocorrelation function (PACF). Finally, the Sparrow search algorithm (SSA) is introduced to optimize the extreme learning machine (ELM) model, which is applied in the carbon price prediction for the purpose of verifying the validity of the proposed combination model, which is applied to the pilots of Hubei, Beijing, and Guangdong. The empirical results show that the combination model outperformed the 13 other models in predicting accuracy, speed, and stability. The decomposition-reconstruction-prediction-integration strategy is a method for predicting the carbon price efficiently.

scholarly journals A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1328
Author(s):  
Jianguo Zhou ◽  
Shiguo Wang
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Influencing Factors ◽  
Search Algorithm ◽  
Carbon Price ◽  
Carbon Trading ◽  
Price Prediction ◽  
Model Based ◽  
Mode Decomposition ◽  
Learning Machine

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.

Empirical analysis: stock market prediction via extreme learning machine

2014 ◽  
Vol 27 (1) ◽  
pp. 67-78 ◽  
Author(s):  
Xiaodong Li ◽  
Haoran Xie ◽  
Ran Wang ◽  
Yi Cai ◽  
Jingjing Cao ◽  
...  
Keyword(s):  
Stock Market ◽  
Extreme Learning Machine ◽  
Empirical Analysis ◽  
Stock Market Prediction ◽  
Learning Machine

Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power

2021 ◽  
pp. 115579
Author(s):  
Ling-Ling Li ◽  
Zhi-Feng Liu ◽  
Ming-Lang Tseng ◽  
Korbkul Jantarakolica ◽  
Ming K. Lim
Keyword(s):  
Wind Power ◽  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Algorithm Optimization ◽  
Short Term ◽  
Machine Model ◽  
Learning Machine

Prediction of Software Defect Using Ensemble Learning Based Improved Sparrow Search Algorithm To Optimize Extreme Learning Machine

2021 ◽  
Author(s):  
Yu Tang ◽  
Qi Dai ◽  
Mengyuan Yang ◽  
Lifang Chen
Keyword(s):  
Extreme Learning Machine ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Search Algorithm ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Prediction Ability ◽  
Software Defect ◽  
Ensemble Learning Algorithm ◽  
Learning Machine

Abstract For the traditional ensemble learning algorithm of software defect prediction, the base predictor exists the problem that too many parameters are difficult to optimize, resulting in the optimized performance of the model unable to be obtained. An ensemble learning algorithm for software defect prediction that is proposed by using the improved sparrow search algorithm to optimize the extreme learning machine, which divided into three parts. Firstly, the improved sparrow search algorithm (ISSA) is proposed to improve the optimization ability and convergence speed, and the performance of the improved sparrow search algorithm is tested by using eight benchmark test functions. Secondly, ISSA is used to optimize extreme learning machine (ISSA-ELM) to improve the prediction ability. Finally, the optimized ensemble learning algorithm (ISSA-ELM-Bagging) is presented in the Bagging algorithm which improve the prediction performance of ELM in software defect datasets. Experiments are carried out in six groups of software defect datasets. The experimental results show that ISSA-ELM-Bagging ensemble learning algorithm is significantly better than the other four comparison algorithms under the six evaluation indexes of Precision, Recall, F-measure, MCC, Accuracy and G-mean, which has better stability and generalization ability.

scholarly journals Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1362 ◽  
Author(s):  
Lu Chen ◽  
Na Sun ◽  
Chao Zhou ◽  
Jianzhong Zhou ◽  
Yanlai Zhou ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Flood Control ◽  
Search Algorithm ◽  
Flood Forecasting ◽  
Data Driven ◽  
General Regression Neural Network ◽  
Promising Alternative ◽  
Backtracking Search ◽  
Learning Machine ◽  
Population Based Algorithm

Flood forecasting plays an important role in flood control and water resources management. Recently, the data-driven models with a simpler model structure and lower data requirement attract much more attentions. An extreme learning machine (ELM) method, as a typical data-driven method, with the advantages of a faster learning process and stronger generalization ability, has been taken as an effective tool for flood forecasting. However, an ELM model may suffer from local minima in some cases because of its random generation of input weights and hidden layer biases, which results in uncertainties in the flood forecasting model. Therefore, we proposed an improved ELM model for short-term flood forecasting, in which an emerging dual population-based algorithm, named backtracking search algorithm (BSA), was applied to optimize the parameters of ELM. Thus, the proposed method is called ELM-BSA. The upper Yangtze River was selected as a case study. Several performance indexes were used to evaluate the efficiency of the proposed ELM-BSA model. Then the proposed model was compared with the currently used general regression neural network (GRNN) and ELM models. Results show that the ELM-BSA can always provide better results than the GRNN and ELM models in both the training and testing periods. All these results suggest that the proposed ELM-BSA model is a promising alternative technique for flood forecasting.

Lithium-ion battery state of health estimation based on improved deep extreme learning machine

2021 ◽  
pp. 1-12
Author(s):  
Yu Zhang ◽  
Wanwan Zeng ◽  
Chun Chang ◽  
Qiyue Wang ◽  
Si Xu
Keyword(s):  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Lithium Ion ◽  
Accurate Estimation ◽  
Time Interval ◽  
State Of Health ◽  
Discharge Data ◽  
Ergodic Method ◽  
Discharge Temperature ◽  
Learning Machine

Abstract Accurate estimation of the state of health (SOH) is an important guarantee for safe and reliable battery operation. In this paper, an online method based on indirect health features (IHF) and sparrow search algorithm fused with deep extreme learning machine (SSA-DELM) of lithium-ion batteries is proposed to estimate SOH. Firstly, the temperature and voltage curves in the battery discharge data are acquired, and the optimal intervals are obtained by ergodic method. Discharge temperature difference at equal time intervals (DTD-ETI) and discharge time interval with equal voltage difference (DTI-EVD) are extracted as IHF. Then, the input weights and hidden layer thresholds of the DELM algorithm are optimized using SSA, and the SSA-DELM model is applied to the estimation of battery's SOH. Finally, the established model is experimentally validated using the battery data, and the results show that the method has high prediction accuracy, strong algorithmic stability and good adaptability.

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