An improved framework to predict river flow time series data

Nazir, Hafiza Mamona; Hussain, Ijaz; Ahmad, Ishfaq; Faisal, Muhammad; Almanjahie, Ibrahim M.

doi:10.7717/peerj.7183

Cited by 12 publications

(7 citation statements)

References 35 publications

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“…Furthermore, it solves the problem of low decomposition efficiency and saves a great deal of processing power. Again, the output of CEEMDAN follows a Gaussian distribution, so that each IMF follows N(θi, 1) [73].…”

Section: Ceemdan Empirical Mode Decomposition Techniquesmentioning

confidence: 99%

A CEEMDAN-Based Entropy Approach Measuring Multiscale Information Flow between Macroeconomic Conditions and Stock Returns of BRICS

et al. 2022

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We model a mixture of asymmetric and nonlinear bidirectional and unidirectional causality between four macroeconomic variables (exchange rate, GDP, global economic policy uncertainty, and relative CPI) and stock returns of BRICS economies in the frequency-domain using the information flow theory. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based Rényi effective transfer entropy approach is used to establish dynamic flow of information between macroeconomic variables and stock returns of BRICS. The original return series suggested insignificant information flow between most macroeconomic variables and stock returns. However, we reveal both asymmetric and tail dependent analyses at diverse scales between macroeconomic variables and stock returns of BRICS economies. Moreover, we find negative significant flow of information between the variables, in that knowing the history of one variable (either stock or macroeconomic variable), in this case, indicates considerably more uncertainty than knowing the history of only the other variable (either stock or macroeconomic variable). We also observe that global economic policy uncertainty has the most significant adverse causal relationship with stock returns of BRICS, especially in the long term. These results have important implications that investors and policymakers should take into account. Regulators should consider instituting sound policy actions geared towards minimising long-term effects of external shocks and uncertainties.

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Section: Ceemdan Empirical Mode Decomposition Techniquesmentioning

confidence: 99%

A CEEMDAN-Based Entropy Approach Measuring Multiscale Information Flow between Macroeconomic Conditions and Stock Returns of BRICS

et al. 2022

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“…ey said that EBT could efficiently tackle the problem of noises by taking different priors for each level. Nazir et al [41] used EBT with CEEMDAN to decompose the river flow time-series data.…”

Section: Introductionmentioning

confidence: 99%

“…ey concluded that their experimental results showed that their proposed strategy achieved better results than other states of arts. Nazir et al [41] used CEEMDAN with EBT to tackle the multiscale and noise complexity of hydrological time-series data, which decompose the nonstationary data into different noise dominating and noise-free IMFs. ey concluded that their proposed model provides efficient prediction results for nonstationary time-series data compared to HT, ST, and ITF by using different evaluation criteria.…”

Section: Introductionmentioning

confidence: 99%

Improvement towards Prediction Accuracy of Principle Mineral Resources Using Threshold

Qurban

Almazah

Nazir³

et al. 2022

Mathematical Problems in Engineering

Self Cite

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The production data of mineral resources are noisy, nonstationary, and nonlinear. Therefore, some techniques are required to address the problem of nonstationarity and complexity of noises in it. In this paper, two hybrid models (EMD-CEEMDAN-EBT-MM and WA-CEEMDAN-EBT-MM) flourish to improve mineral production prediction. First, we use empirical mode decomposition (EMD) and wavelet analysis (WA) to denoise the data. Second, ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition (CEEMDAN) are used for the decomposition of nonstationary data into intrinsic mode function (IMF). Then, empirical Bayesian threshold (EBT) is applied on noise dominant IMFs to consolidate noises, which are further used as input in the data-driven model. Next, other noise-free IMFs are used in the stochastic model as input for the prediction of minerals. At last, the predicted IMFs are ensemble for final prediction. The proposed strategy is exemplified using Pakistan's four major mineral resources. To measure the prediction performance of all the models, three methods, that is, mean relative error, mean square error, and mean absolute percentage error, are used. Our proposed framework WA-CEEMDAN-EBT-MM has shown improvement with minimum mean absolute percentage error value compared to other existing models in prediction accuracy for all four minerals. Therefore, our proposed strategy can predict the noisy and nonstationary time-series data with an efficient mechanism. Hence, it will be helpful to the policymakers for making policies and planning in mineral resource management.

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“…Although this prediction method relying solely on the neural network model has achieved good prediction results, it does not consider importance of data preprocessing. In recent years, the decomposition technology in data preprocessing has attracted the attention of researchers, and some achievements have been made in time series prediction [13][14][15][16][17][18][19]. Li et al [13] proposed a chaotic time series prediction model of monthly precipitation based on the combination of variational mode decomposition and extreme learning machine (ELM).…”

Section: Introductionmentioning

confidence: 99%

“…Cheng et al [17] used ensemble empirical mode decomposition and LSSVM to achieve short-term prediction of wind power and verified that this prediction method has better prediction accuracy than EMD and LSSVM methods. Nazir et al [18] proposed an improved CEEMDAN decomposition method and applied it to the prediction of river flow, and achieved good prediction results. Li et al [19] proposed an underwater acoustic signal prediction method based on ESMD and ELM, which further improved the prediction accuracy based on ELM.…”

Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine

Yang

Gao

2020

Complexity

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Aiming at the chaotic characteristics of underwater acoustic signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of underwater acoustic signals. To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. Based on the prediction model of kernel extreme learning machine (KELM), this paper uses grey wolf optimization (GWO) algorithm to optimize and select its regularization parameters and kernel parameters and proposes an optimized kernel extreme learning machine OKELM. To further improve the prediction performance of the model, combined with MVMD, an underwater acoustic signal prediction model based on MVMD-OKELM is established. MVMD-OKELM prediction model is applied to Mackey–Glass chaotic time series prediction and underwater acoustic signal prediction and is compared with ARIMA, EMD-OKELM, and other prediction models. The experimental results show that the proposed MVMD-OKELM prediction model has a higher prediction accuracy and can be effectively applied to the prediction of underwater acoustic signal series.

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An improved framework to predict river flow time series data

Cited by 12 publications

References 35 publications

A CEEMDAN-Based Entropy Approach Measuring Multiscale Information Flow between Macroeconomic Conditions and Stock Returns of BRICS

A CEEMDAN-Based Entropy Approach Measuring Multiscale Information Flow between Macroeconomic Conditions and Stock Returns of BRICS

Improvement towards Prediction Accuracy of Principle Mineral Resources Using Threshold

Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine

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