A Deep Learning Prediction Model Based on Extreme‐Point Symmetric Mode Decomposition and Cluster Analysis

Abstract

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extremepoint symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy -means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed IMFs and residuals are the final prediction results. Six kinds of prediction models are compared, which are DBN prediction model, EMD-DBN prediction model, EEMD-DBN prediction model, CEEMD-DBN prediction model, ESMD-DBN prediction model, and the proposed model in this paper. The same sunspots time series are predicted with six kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

“…To objectively evaluate the accuracy of the prediction results, it is necessary to establish corresponding evaluation indicators to verify the effectiveness and feasibility of the proposed experimental method. This paper aims to adopt the mean square error (MSE), the mean absolute percentage error (MAPE), the coefficient of determination (R 2 ), and RMSE as the evaluation index for assessing the accuracy of predictions [37,38]. The specific calculation equations are as follows: 22)…”

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“…Hence, ESMD is very suitable to analyze nonlinear and non-stationary series. While this method has been successfully used in broad fields such as economics, medicine, atmospheric science, and hydrology (Li et al 2017;Lin et al 2017;Zhou et al 2019a), few attempts have been made to use the latest advance in ESMD to solve the problem of hydrological time series prediction. Therefore, an objective of this article is to explore the efficiency of ESMD in capturing hydrological time series characteristics.…”

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

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“…Büyükşahin and Ertekin [15] proposed a hybrid prediction method combining ARIMA and artificial neural network (ANN) and added empirical mode decomposition technology to further improve the prediction accuracy of time series. Li et al [16] proposed a deep learning prediction model based on extreme-point symmetric mode decomposition and cluster analysis to predict monthly mean value of sunspots and have a good prediction effect. 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.…”