☆ 4.7 Article

M-EDEM: A MNN-based Empirical Decomposition Ensemble Method for improved time series forecasting

KNOWLEDGE-BASED SYSTEMS (2024)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Automation & Control Systems

Differential Evolution-Based Three Stage Dynamic Cyber-Attack of Cyber-Physical Power Systems

Kang-Di Lu et al.

Summary: This article proposes a novel three-stage dynamic false data injection attack (DFDIA) model in cyber-physical power systems (CPPS) by considering potential dynamic behaviors. It formulates the designing DFDIA as two constrained single-objective optimization problems and presents two versions of constrained differential evolution as solvers. It also proposes an interval state forecasting-based countermeasure to detect the established DFDIA and demonstrates their feasibility and effectiveness through extensive simulation experiments on IEEE bus systems.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2023)

添加到收藏夹

Article Thermodynamics

Multi-step wind speed forecasting and Hurst analysis using novel hybrid secondary decomposition approach

Cem Emeksiz et al.

Summary: A hybrid model combining CEEMDAN, LMD, Hurst, and BPNN is proposed for wind speed prediction, showing better accuracy compared to traditional methods with a decrease in MAPE values by 41.16% and 78.80%.

ENERGY (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A new scheme for probabilistic forecasting with an ensemble model based on CEEMDAN and AM-MCMC and its application in precipitation forecasting

Yinan Wang et al.

Summary: A new scheme for probabilistic precipitation forecasting is proposed in this study, which uses signal decomposition techniques and ensemble models to improve forecasting accuracy and probabilistic metrics compared to single-model predictions.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

添加到收藏夹

Article Energy & Fuels

Carbon price forecasting based on CEEMDAN and LSTM

Feite Zhou et al.

Summary: After signing the Paris Agreement and piloting carbon trading for many years, China has taken a significant step toward carbon neutrality. This paper builds multiple predictors to analyze and forecast carbon prices, providing practical guidance for production, operation, and investment.

APPLIED ENERGY (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Modeling and predicting rainfall time series using seasonal-trend decomposition and machine learning

Renfei He et al.

Summary: This study presents a hybrid approach (STL-ML) that integrates seasonal-trend decomposition and machine learning for predicting rainfall time series. A case study using meteorological data from Cairns, Australia demonstrates the accuracy and reliability of the proposed approach, especially for sudden extreme rainfall events. Comparisons with baseline methods further support the effectiveness of the hybrid STL-ML approach.

KNOWLEDGE-BASED SYSTEMS (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

The potential of integrated hybrid data processing techniques for successive-station streamflow prediction

Roghayeh Ghasempour et al.

Summary: This study evaluated the capability of newly integrated hybrid prediction models based on artificial intelligence and data processing methods for monthly river streamflow modeling. The integrated pre-post-processing models improved the models efficiency by approximately 45% and allowed the successful application of upstream stations data for streamflow modeling.

SOFT COMPUTING (2022)

添加到收藏夹

Article Environmental Sciences

CEEMD-MR-hybrid model based on sample entropy and random forest for SO2 prediction

Suling Zhu et al.

Summary: This study proposes a CEEMD-MR-Hybrid model for SO2 forecasting, which includes CEEMD, machine learning models, and mode refactor system. By reconstructing the decomposed series using sample entropy and random forest, this model improves the accuracy of the forecasting.

ATMOSPHERIC POLLUTION RESEARCH (2022)

添加到收藏夹

Article Environmental Sciences

A Tailings Dam Long-Term Deformation Prediction Method Based on Empirical Mode Decomposition and LSTM Model Combined with Attention Mechanism

Yang Zhu et al.

Summary: This paper proposes a new method for predicting tailings dam deformation, combining empirical mode decomposition and attention mechanism, which can effectively solve problems such as gradient disappearance and gradient explosion, and has high prediction accuracy and practical application effect.

WATER (2022)

添加到收藏夹

Article Water Resources

Can sampling techniques improve the performance of decomposition-based hydrological prediction models? Exploration of some comparative experiments

Miao He et al.

Summary: The development of sequence decomposition techniques has greatly facilitated the use of decomposition-based prediction models in hydrological forecasting. However, the current overall decomposition (OD) sampling technique used in these models has some limitations. This study proposes and evaluates novel sampling techniques and applies them to predict monthly runoff in Poyang Lake, China. The results show that the models developed using the improved sampling techniques have superior performance.

APPLIED WATER SCIENCE (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A novel decomposition-ensemble forecasting system for dynamic dispatching of smart grid with sub-model selection and intelligent optimization

Jianzhou Wang et al.

Summary: This paper proposes a hybrid ensemble forecasting scheme to enrich the current load forecasting system, which incorporates sub-model selection, data decomposition, multi-objective optimization, point prediction, and interval prediction. Experimental results show that the hybrid ensemble system provides better point and interval forecasting performance.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

添加到收藏夹

Article Engineering, Electrical & Electronic

Boundary Effects for EMD-Based Algorithms

Yung-Hung Wang et al.

Summary: Empirical mode decomposition (EMD) and its improved algorithms are able to decompose nonstationary signals into intrinsic mode functions (IMFs). However, the presence of noise can introduce errors in the decomposition. This study provides a mathematical analysis of the boundary effects on error propagation in EMD-based algorithms and proposes a method to address error amplification.

IEEE SIGNAL PROCESSING LETTERS (2022)

添加到收藏夹

Article Mathematical & Computational Biology

Runoff forecasting model based on variational mode decomposition and artificial neural networks

Xin Jing et al.

Summary: This paper proposes a novel hybrid runoff forecasting model based on variational mode decomposition, convolutional neural networks, and long short-term memory. By considering potential correlation information, the model improves the runoff forecasting performance. Experimental results demonstrate the superiority and stability of the model compared to baseline models.

MATHEMATICAL BIOSCIENCES AND ENGINEERING (2022)

添加到收藏夹

Article Thermodynamics

An effective rolling decomposition-ensemble model for gasoline consumption forecasting

Lean Yu et al.

Summary: This paper proposes an effective rolling decomposition-ensemble model for quarterly gasoline consumption forecasting in China, involving data decomposition, component prediction, and ensemble output. By utilizing wavelet decomposition and support vector regression, the model addresses data scarcity issue and improves prediction accuracy.

ENERGY (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Forecasting Stock Index Using a Volume-Aware Positional Attention-Based Recurrent Neural Network

Xinpeng Yu et al.

Summary: Researchers found that traditional attention mechanisms in stock market trend prediction often overlook important factors like trading volume, leading to the proposal of a VPA-RNN method that incorporates these factors to improve model performance.

INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING (2021)

添加到收藏夹

Article Computer Science, Information Systems

An Improved Decomposition-Based Multiobjective Evolutionary Algorithm for IoT Service

Zheng-Yi Chai et al.

Summary: This paper presents an improved decomposition-based multiobjective evolutionary algorithm for optimizing the allocation of IoT services. By designing appropriate operators, better solutions can be achieved in agricultural IoT services, reducing service time and lowering service costs.

IEEE INTERNET OF THINGS JOURNAL (2021)

添加到收藏夹

Article Engineering, Civil

Ensemble stationary-based support vector regression for drought prediction under changing climate

Mohammad Hadi Bazrkar et al.

Summary: This study aims to improve drought prediction by eliminating non-stationarity from temperature time series, with the application in the Red River of the North Basin showing that the ESSVR method outperforms the traditional SVR.

JOURNAL OF HYDROLOGY (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A new secondary decomposition ensemble learning approach for carbon price forecasting

Hongtao Li et al.

Summary: The paper presents a new secondary decomposition strategy for forecasting carbon prices, showing superior performance in multistep forecasting compared to benchmark models.

KNOWLEDGE-BASED SYSTEMS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A new crude oil price forecasting model based on variational mode decomposition

Yusheng Huang et al.

Summary: An improved VMD-parameter selection rule and signal-energy based moving-window strategy were proposed, and a VMD-LSTM-MW model was established, demonstrating its effectiveness, validity, and superiority through experiments.

KNOWLEDGE-BASED SYSTEMS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Stock price prediction using deep learning and frequency decomposition

Hadi Rezaei et al.

Summary: The combination of new deep learning and decomposition algorithms has improved the accuracy and performance of financial time series analysis. Combining CEEMD with CNN and LSTM can produce better prediction results.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

Network traffic prediction model based on ensemble empirical mode decomposition and multiple models

Lian Lian et al.

Summary: A novel prediction model based on ensemble empirical mode decomposition and multiple models is proposed to improve the prediction accuracy of network traffic. The complexity of each component is judged by introducing approximate entropy, and different prediction models are used to predict components with different complexities. An improved whale optimization algorithm is utilized to optimize model parameters for enhancing prediction accuracy. Experimental results demonstrate that the proposed model outperforms other models in terms of prediction accuracy and statistical performance indicators.

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Machine learning for precision medicine forecasts and challenges when incorporating non omics and omics data

J. Susymary et al.

Summary: Precision Medicine, integrating omics and non-omics data, presents advantages for those living in industrially polluted areas. The challenges of integrating heterogeneous non-omics data and high dimensional omics data create opportunities for analysis.

INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS (2021)

添加到收藏夹

Article Business

An adaptive decomposition and ensemble model for short-term air pollutant concentration forecast using ICEEMDAN-ICA

Yu-jie Xiao et al.

Summary: This study highlights the importance of precise short-term atmospheric pollutant concentration forecasting and addresses the boundary effect in decomposition results. By proposing an adaptive forecasting scheme and introducing ICA, the study develops an adaptive decomposition and ensemble model that demonstrates superior performance in predicting pollutant concentrations.

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

An Efficient Forecasting Approach to Reduce Boundary Effects in Real-Time Time-Frequency Analysis

Adrien Meynard et al.

Summary: This study introduces an effective method to reduce the boundary effects in time-frequency representations of time series, which is suitable for real-time acquisition of information from nonstationary oscillatory time series. The theoretical guarantee of the method's performance is provided through the study of locally oscillating signals and its effectiveness is validated by implementing it on biomedical signals after numerical verification of algorithmic performance.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network

Yishun Liu et al.

KNOWLEDGE-BASED SYSTEMS (2020)

添加到收藏夹

Article Engineering, Civil

Decomposition ensemble model based on variational mode decomposition and long short-term memory for streamflow forecasting

Ganggang Zuo et al.

JOURNAL OF HYDROLOGY (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

An Adaptive CEEMDAN Thresholding Denoising Method Optimized by Nonlocal Means Algorithm

Shuqing Zhang et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

添加到收藏夹

Article Geosciences, Multidisciplinary

Two-stage variational mode decomposition and support vector regression for streamflow forecasting

Ganggang Zuo et al.

HYDROLOGY AND EARTH SYSTEM SCIENCES (2020)

添加到收藏夹

Article Energy & Fuels

A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting

Xinsong Niu et al.

APPLIED ENERGY (2019)

添加到收藏夹

Article Thermodynamics

A novel probabilistic wind speed prediction approach using real time refined variational model decomposition and conditional kernel density estimation

Yan Jiang et al.

ENERGY CONVERSION AND MANAGEMENT (2019)

添加到收藏夹

Article Computer Science, Information Systems

A Two-Layer Nonlinear Combination Method for Short-Term Wind Speed Prediction Based on ELM, ENN, and LSTM

Min-Rong Chen et al.

IEEE INTERNET OF THINGS JOURNAL (2019)

添加到收藏夹

Article Engineering, Civil

Examining the applicability of different sampling techniques in the development of decomposition-based streamflow forecasting models

Wei Fang et al.

JOURNAL OF HYDROLOGY (2019)

添加到收藏夹

Article Engineering, Civil

An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach

Qiao-Feng Tan et al.

JOURNAL OF HYDROLOGY (2018)

添加到收藏夹

Article Engineering, Civil

Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework

John Quilty et al.

JOURNAL OF HYDROLOGY (2018)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A new weighted CEEMDAN-based prediction model: An experimental investigation of decomposition and non-decomposition approaches

Wang Jun et al.

KNOWLEDGE-BASED SYSTEMS (2018)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A weighted EMD-based prediction model based on TOPSIS and feed forward neural network for noised time series

Wang Jun et al.

KNOWLEDGE-BASED SYSTEMS (2017)

添加到收藏夹

Article Physics, Multidisciplinary

Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis

E. Jianwei et al.

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2017)

添加到收藏夹

Article Environmental Sciences

A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

Mingfei Niu et al.

ATMOSPHERIC ENVIRONMENT (2016)

添加到收藏夹

Article Engineering, Civil

Are hybrid models integrated with data preprocessing techniques suitable for monthly streamflow forecasting? Some experiment evidences

Xiaoli Zhang et al.

JOURNAL OF HYDROLOGY (2015)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Short term load forecasting using a hybrid intelligent method

Adel Abdoos et al.

KNOWLEDGE-BASED SYSTEMS (2015)

添加到收藏夹

Article Engineering, Electrical & Electronic

Variational Mode Decomposition

Konstantin Dragomiretskiy et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2014)

添加到收藏夹

Article Engineering, Electrical & Electronic

Empirical Wavelet Transform

Jerome Gilles

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2013)

添加到收藏夹

Article Engineering, Civil

Predictability of nonstationary time series using wavelet and EMD based ARMA models

L. Karthikeyan et al.

JOURNAL OF HYDROLOGY (2013)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Energy Time Series Forecasting Based on Pattern Sequence Similarity

Francisco Martinez-Alvarez et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2011)

添加到收藏夹

Article Computer Science, Information Systems

GPGPU-Aided Ensemble Empirical-Mode Decomposition for EEG Analysis During Anesthesia

Dan Chen et al.

IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE (2010)

添加到收藏夹

Article Engineering, Mechanical

On the HHT its problems, and some solutions

R. T. Rato et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2008)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.