Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis

Bisoi, Ranjeeta; Dash, P.K.; Parida, A.K.

doi:10.1016/j.asoc.2018.11.008

Cited by 117 publications

(41 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In recent years, the time series prediction based on VMD and ELM has caught many scholars' attentions. Literature [31] used Robust Kernel based Extreme Learning Machine (RKELM) integrated with VMD to predict stock price and movement, but they did not consider the phase space reconstruction of each subset and the optimized selection of two control parameters. Literature [32] combined the VMD, Singular Spectrum Analysis (SSA), Long Short Term Memory (LSTM) network and ELM to forecast wind speed, but many tuning parameters of the VMD, ELM were not optimized for better choice.…”

Section: B Related Workmentioning

confidence: 99%

Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction

Shi

Leau

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Network traffic prediction plays a vital role in effective network management, load evaluation and security warning. Extreme learning machine has the advantages of fast convergence speed and strong generalization ability. Also, it does not easily fall into local optima. The evolutionary algorithm can be used to optimize the number of its hidden layer nodes. However, most of the existing evolutionary algorithms have some adjustable parameters which depend on subjective experience or prior knowledge. Hence, this can affect the model prediction accuracy. Given this context, this paper proposes a network traffic prediction mechanism based on optimized Variational Mode Decomposition (VMD) and Integrated Extreme Learning Machine (ELM). A Scalable Artificial Bee Colony (SABC) algorithm which has fewer adjustable parameters and can thus guarantee the accuracy and stability of the prediction mechanism is also proposed. It can be used in the optimization selection of VMD, Phase Space Reconstruction (PSR) and ELM to achieve higher prediction performance. Finally, we utilize Mackey-Glass, Lorenz chaotic time series of recognized benchmark and a WIDE backbone actual network traffic data to prove the validity of the proposed network traffic prediction mechanism.

show abstract

Section: B Related Workmentioning

confidence: 99%

Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction

Shi

Leau

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…This method has attracted much attention due to its solid theoretical foundation, strong noise robustness and precise component separation [41]. The hybrids AI and VMD models have successfully been employed in power quality events recognition [42], short-term load forecasting [43], time frequency analysis of Mirnov coil [44], stock price and movement prediction [45], short-term wind power generation forecasting [46], wind speed forecasting [47], and solar radiation forecasting [48]. In the hydrological domain, runoff and rainfall-runoff predictions were mainly focused upon.…”

Section: Introductionmentioning

confidence: 99%

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

Feng

Wen

et al. 2021

Water

View full text Add to dashboard Cite

Precise multi-time scales prediction of groundwater level is essential for water resources planning and management. However, credible and reliable predicting results are hard to achieve even to extensively applied artificial intelligence (AI) models considering the uncontrollable error, indefinite inputs and unneglectable uncertainty during the modelling process. The AI model ensembled with the data pretreatment technique, the input selection method, or uncertainty analysis has been successfully used to tackle this issue, whereas studies about the comprehensive deterministic and uncertainty analysis of hybrid models in groundwater level forecast are rarely reported. In this study, a novel hybrid predictive model combining the variational mode decomposition (VMD) data pretreatment technique, Boruta input selection method, bootstrap based uncertainty analysis, and the extreme learning machine (ELM) model named VBELM was developed for 1-, 2- and 3-month ahead groundwater level prediction in a typical arid oasis area of northwestern China. The historical observed monthly groundwater level, precipitation and temperature data were used as inputs to train and test the model. Specifically, the VMD was used to decompose all the input-outputs into a set of intrinsic mode functions (IMFs), the Boruta method was applied to determine input variables, and the ELM was employed to forecast the value of each IMF. In order to ascertain the efficiency of the proposed VBELM model, the performance of the coupled model (VELM) hybridizing VMD with ELM algorithm and the single ELM model were estimated in comparison. The results indicate that the VBELM performed best, while the single ELM model performed the worst among the three models. Furthermore, the VBELM model presented lower uncertainty than the VELM model with more observed groundwater level values falling inside the confidence interval. In summary, the VBELM model demonstrated an excellent performance for both certainty and uncertainty analyses, and can serve as an effective tool for multi-scale groundwater level forecasting.

show abstract

“…e empirical results show that compared with the benchmark model PSO-BPNN, the VMD-PSO-BPNN model has advantages in prediction performance. Bisoi et al [50] combined the variational model decomposition (VMD) and the optimized limit learning organization to build a DE-VMD-RKELM model to predict the BSE S&P 500 Index (BSE), Hang Seng Index (HSI), and Financial Times Stock Exchange 100 Index (FTSE). Wang et al [51], based on fast ensemble empirical mode decomposition (FEEMD), VMD, and backpropagation (BP) neural network, established a mixed model of two-level decomposition to predict the electricity price.…”

Section: Introductionmentioning

confidence: 99%

Multistep-Ahead Stock Price Forecasting Based on Secondary Decomposition Technique and Extreme Learning Machine Optimized by the Differential Evolution Algorithm

Tang

Zhang

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The prediction research of the stock market prices is of great significance. Based on the secondary decomposition techniques of variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD), this paper constructs a new hybrid prediction model by combining with extreme learning machine (ELM) optimized by the differential evolution (DE) algorithm. The hybrid model applies VMD technology to the original stock index price sequence to obtain different modal components and the residual item, then applies EEMD technology to the residual item, and then superimposes the prediction results of the DE-ELM model for each modal component and the residual item to obtain the final prediction results. In order to verify the validity of the model, this paper constructs a series of benchmark models and, respectively, tests the samples of the S&P 500 index and the HS300 index by one-step, three-step, and five-step forward forecasting. The empirical results show that the hybrid model proposed in this paper achieves the best prediction performance in all prediction scenarios, which indicates that the modeling idea focusing on the residual term effectively improves the prediction performance of the model. In addition, the prediction effect of the model combined with the decomposition technology is superior to the single DE-ELM model, where the secondary decomposition technique has a significant decomposition advantage compared to the single decomposition technique.

show abstract

Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis

Cited by 117 publications

References 55 publications

Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction

Optimal Variational Mode Decomposition and Integrated Extreme Learning Machine for Network Traffic Prediction

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

Multistep-Ahead Stock Price Forecasting Based on Secondary Decomposition Technique and Extreme Learning Machine Optimized by the Differential Evolution Algorithm

Contact Info

Product

Resources

About