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Relevant bibliographies by topics / EMD - Neural networks

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Journal articles

Academic literature on the topic 'EMD - Neural networks'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "EMD - Neural networks"

1

Zheng, Jing Wen, Shi Xiao Li, and Yang Kun. "A New Hybrid Model for Forecasting Crude Oil Price and the Techniques in the Model." Advanced Materials Research 974 (June 2014): 310–17. http://dx.doi.org/10.4028/www.scientific.net/amr.974.310.

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Being able to predict crude oil prices with a reputation of intransigence to analysis or the directions of changing in crude oil price is of increasing value. We seek a method to forecast oil prices with precise predictions. In this paper, a hybrid model was proposed, which firstly decomposes the crude oil prices into several time series with different frequencies,then predict these time series which are not white noises, and at last integrate the predictions as the final results. We use Ensemble Empirical Mode Decomposition (EEMD) and Empirical Mode Decomposition (EMD) separately as the techn

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2

Saâdaoui, Foued, and Othman Ben Messaoud. "Multiscaled Neural Autoregressive Distributed Lag: A New Empirical Mode Decomposition Model for Nonlinear Time Series Forecasting." International Journal of Neural Systems 30, no. 08 (2020): 2050039. http://dx.doi.org/10.1142/s0129065720500392.

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Forecasting has always been the cornerstone of machine learning and statistics. Despite the great evolution of the time series theory, forecasters are still in the hunt for better models to make more accurate decisions. The huge advances in neural networks over the last years has led to the emergence of a new generation of effective models replacing classic econometric models. It is in this direction that we propose, in this paper, a new multiscaled Feedforward Neural Network (FNN), with the aim of forecasting multivariate time series. This new model, called Empirical Mode Decomposition (EMD)-

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3

Lei, Yu, Danning Zhao, and Hongbing Cai. "Ultra Short-term Prediction of Pole Coordinates via Combination of Empirical Mode Decomposition and Neural Networks." Artificial Satellites 51, no. 4 (2016): 149–61. http://dx.doi.org/10.1515/arsa-2016-0013.

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Abstract It was shown in the previous study that the increase of pole coordinates prediction error for about 100 days in the future is mostly caused by irregular short period oscillations. In this paper, the ultra short-term prediction of pole coordinates is studied for 10 days in the future by means of combination of empirical mode decomposition (EMD) and neural networks (NN), denoted EMD-NN. In the algorithm, EMD is employed as a low pass filter for eliminating high frequency signals from observed pole coordinates data. Then the annual and Chandler wobbles are removed a priori from pole coor

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4

Ge, Yujia, Yurong Nan, and Lijun Bai. "A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor Networks." Energies 12, no. 24 (2019): 4762. http://dx.doi.org/10.3390/en12244762.

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For power management in the energy harvesting wireless sensor networks (EH-WSNs), it is necessary to know in advance the collectable solar energy data of each node in the network. Our work aims to improve the accuracy of solar energy predictions. Therefore, several existing prediction algorithms in the literature are surveyed, and then this paper proposes a solar radiance prediction model based on a long short-term memory (LSTM) neural network in combination with the signal processing algorithm empirical mode decomposition (EMD). The EMD method is used to decompose the time sequence data into

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5

Jiang, Qi, Yuxin Cheng, Haozhe Le, Chunquan Li, and Peter X. Liu. "A Stacking Learning Model Based on Multiple Similar Days for Short-Term Load Forecasting." Mathematics 10, no. 14 (2022): 2446. http://dx.doi.org/10.3390/math10142446.

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It is challenging to obtain accurate and efficient predictions in short-term load forecasting (STLF) systems due to the complexity and nonlinearity of the electric load signals. To address these problems, we propose a hybrid predictive model that includes a sliding-window algorithm, a stacking ensemble neural network, and a similar-days predictive method. First, we leverage a sliding-window algorithm to process the time-series electric load data with high nonlinearity and non-stationarity. Second, we propose an ensemble learning scheme of stacking neural networks to improve forecasting perform

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6

Huang, Xiaoxin, and Xiuxiu Chen. "A Quantitative Model of International Trade Based on Deep Neural Network." Computational Intelligence and Neuroscience 2022 (May 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/9811358.

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This paper is an in-depth study of international trade quantification models based on deep neural networks. Based on an in-depth analysis of global trade characteristics, a summary of existing problems, and a comparative analysis of various prediction methods, this paper constructs the ARIMA model, BP neural network (BPNN) model, and deep neural network (DNN) model to make a comprehensive comparison of international trade quantification. The results show that the nonlinear model has a global trade quantification has some advantages over linear models, and the deep model shows better prediction

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7

Zhou, Shuyi, Brandon J. Bethel, Wenjin Sun, Yang Zhao, Wenhong Xie, and Changming Dong. "Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network." Journal of Marine Science and Engineering 9, no. 7 (2021): 744. http://dx.doi.org/10.3390/jmse9070744.

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Wave forecasts, though integral to ocean engineering activities, are often conducted using computationally expensive and time-consuming numerical models with accuracies that are blunted by numerical-model-inherent limitations. Additionally, artificial neural networks, though significantly computationally cheaper, faster, and effective, also experience difficulties with nonlinearities in the wave generation and evolution processes. To solve both problems, this study employs and couples empirical mode decomposition (EMD) and a long short-term memory (LSTM) network in a joint model for significan

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8

Zhang, Boning. "Foreign exchange rates forecasting with an EMD-LSTM neural networks model." Journal of Physics: Conference Series 1053 (July 2018): 012005. http://dx.doi.org/10.1088/1742-6596/1053/1/012005.

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9

Chengzhao, Zhang, Pan Heiping, and Zhou Ke. "Comparison of Back Propagation Neural Networks and EMD-Based Neural Networks in Forecasting the Three Major Asian Stock Markets." Journal of Applied Sciences 15, no. 1 (2014): 90–99. http://dx.doi.org/10.3923/jas.2015.90.99.

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10

Shu, Wangwei, and Qiang Gao. "Forecasting Stock Price Based on Frequency Components by EMD and Neural Networks." IEEE Access 8 (2020): 206388–95. http://dx.doi.org/10.1109/access.2020.3037681.

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