scholarly journals A Hybrid Model for Monthly Precipitation Time Series Forecasting Based on Variational Mode Decomposition with Extreme Learning Machine

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 177 ◽  
Author(s):  
Guohui Li ◽  
Xiao Ma ◽  
Hong Yang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Hybrid Model ◽  
Prediction Accuracy ◽  
Monthly Precipitation ◽  
Variational Mode Decomposition ◽  
Precipitation Time Series ◽  
Mode Decomposition ◽  
Learning Machine

The matter of success in forecasting precipitation is of great significance to flood control and drought relief, and water resources planning and management. For the nonlinear problem in forecasting precipitation time series, a hybrid prediction model based on variational mode decomposition (VMD) coupled with extreme learning machine (ELM) is proposed to reduce the difficulty in modeling monthly precipitation forecasting and improve the prediction accuracy. The monthly precipitation data in the past 60 years from Yan’an City and Huashan Mountain, Shaanxi Province, are used as cases to test this new hybrid model. First, the nonstationary monthly precipitation time series are decomposed into several relatively stable intrinsic mode functions (IMFs) by using VMD. Then, an ELM prediction model is established for each IMF. Next, the predicted values of these components are accumulated to obtain the final prediction results. Finally, three predictive indicators are adopted to measure the prediction accuracy of the proposed hybrid model, back propagation (BP) neural network, Elman neural network (Elman), ELM, and EMD-ELM models: mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). The experimental simulation results show that the proposed hybrid model has higher prediction accuracy and can be used to predict the monthly precipitation time series.

scholarly journals A Hybrid Model for Forecasting Sunspots Time Series Based on Variational Mode Decomposition and Backpropagation Neural Network Improved by Firefly Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Guohui Li ◽  
Xiao Ma ◽  
Hong Yang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Bp Neural Network ◽  
Firefly Algorithm ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Earth’S Climate ◽  
Bp Model

The change of the number of sunspots has a great impact on the Earth’s climate, agriculture, communications, natural disasters, and other aspects, so it is very important to predict the number of sunspots. Aiming at the chaotic characteristics of monthly mean of sunspots, a novel hybrid model for forecasting sunspots time-series based on variational mode decomposition (VMD) and backpropagation (BP) neural network improved by firefly algorithm (FA) is proposed. Firstly, a set of intrinsic mode functions (IMFs) are obtained by VMD decomposition of the monthly mean time series of the sunspots. Secondly, the firefly algorithm is introduced to initialize the weights and thresholds of the BP neural network, and a prediction model is established for each IMF. Finally, the predicted values of these components are calculated to obtain the final predict results. Comparing BP model, FA-BP model, EMD-BP model, and VMD-BP model, the simulation results show that the proposed algorithm has higher prediction accuracy and can be used to forecast the time series of sunspots.

Ultra-short-term wind speed prediction based on variational mode decomposition and optimized extreme learning machine

2021 ◽  
pp. 0309524X2110385
Author(s):  
Lian Lian ◽  
Kan He
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Good Prediction ◽  
Variational Mode Decomposition ◽  
Short Term ◽  
Mode Decomposition ◽  
Whale Optimization ◽  
Learning Machine

The main purpose of this paper is to improve the prediction accuracy of ultra-short-term wind speed. It is difficult to predict the ultra-short-term wind speed because of its unstable, non-stationary and non-linear. Aiming at the unstable and non-stationary characteristics of ultra-short-term wind speed, the variational mode decomposition algorithm is introduced to decompose the ultra-short-term wind speed data, and a series of stable and stationary components with different frequencies are obtained. The extreme learning machine with good prediction performance and real-time performance is selected as the prediction model of decomposed components. In order to solve the problem of random setting of input weights and bias of extreme learning machine, whale optimization algorithm is used to optimize extreme learning machine to improve the regression performance. The performance of the developed prediciton model is verified by real ultra-short-term wind speed sample data. Five prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, eight performance indicators, and Pearson’s test correlation coefficient, the results show that the proposed prediction model has high prediction accuracy.

scholarly journals Sunspots Time-Series Prediction Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Guohui Li ◽  
Siliang Wang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Empirical Mode Decomposition ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Ensemble Empirical Mode Decomposition ◽  
Wavelet Neural Network ◽  
Hybrid Prediction ◽  
Mode Decomposition

The sunspot numbers are the major target which describes the solar activity level. Long-term prediction of sunspot activity is of great importance for aerospace, communication, disaster prevention, and so on. To improve the prediction accuracy of sunspot time series, the prediction model based on complementary ensemble empirical mode decomposition (CEEMD) and wavelet neural network (WNN) is proposed. First, the sunspot time series are decomposed by CEEMD to obtain a set of intrinsic modal functions (IMFs). Then, the IMFs and residuals are reconstructed to obtain the training samples and the prediction samples, and these samples are trained and predicted by WNN. Finally, the reconstructed IMFs and residuals are the final prediction results. Five kinds of prediction models are compared, which are BP neural network prediction model, WNN prediction model, empirical mode decomposition and WNN hybrid prediction model, ensemble empirical mode decomposition and WNN hybrid prediction model, and the proposed method in this paper. The same sunspot time series are predicted with five kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.

scholarly journals A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 610 ◽  
Author(s):  
Xinghan Xu ◽  
Weijie Ren
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Back Propagation ◽  
Research Field ◽  
Chaotic Time Series ◽  
Good Prospect ◽  
Decomposition Approach ◽  
Model Based ◽  
Mode Decomposition

The prediction of chaotic time series has been a popular research field in recent years. Due to the strong non-stationary and high complexity of the chaotic time series, it is difficult to directly analyze and predict depending on a single model, so the hybrid prediction model has become a promising and favorable alternative. In this paper, we put forward a novel hybrid model based on a two-layer decomposition approach and an optimized back propagation neural network (BPNN). The two-layer decomposition approach is proposed to obtain comprehensive information of the chaotic time series, which is composed of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD). The VMD algorithm is used for further decomposition of the high frequency subsequences obtained by CEEMDAN, after which the prediction performance is significantly improved. We then use the BPNN optimized by a firefly algorithm (FA) for prediction. The experimental results indicate that the two-layer decomposition approach is superior to other competing approaches in terms of four evaluation indexes in one-step and multi-step ahead predictions. The proposed hybrid model has a good prospect in the prediction of chaotic time series.

scholarly journals Improving prediction accuracy of river discharge time series using a Wavelet-NAR artificial neural network

2012 ◽  
Vol 14 (4) ◽  
pp. 974-991 ◽  
Author(s):  
Shouke Wei ◽  
Depeng Zuo ◽  
Jinxi Song
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
River Discharge ◽  
Prediction Accuracy ◽  
Hybrid Approach ◽  
Series Data ◽  
Discharge Time ◽  
Artificial Neural

This study developed a wavelet transformation and nonlinear autoregressive (NAR) artificial neural network (ANN) hybrid modeling approach to improve the prediction accuracy of river discharge time series. Daubechies 5 discrete wavelet was employed to decompose the time series data into subseries with low and high frequency, and these subseries were then used instead of the original data series as the input vectors for the designed NAR network (NARN) with the Bayesian regularization (BR) optimization algorithm. The proposed hybrid approach was applied to make multi-step-ahead predictions of monthly river discharge series in the Weihe River in China. The prediction results of this hybrid model were compared with those of signal NARNs and the traditional Wavelet-Artificial Neural Network hybrid approach (WNN). The comparison results revealed that the proposed hybrid model could significantly increase the prediction accuracy and prediction period of the river discharge time series in the current case study.

Short-Term Wind Speed Combination Prediction Model of Neural Network and Time Series

2012 ◽  
Vol 608-609 ◽  
pp. 764-769
Author(s):  
Hao Zheng ◽  
Jian Yan Tian ◽  
Fang Wang ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Prediction Model ◽  
Wind Direction ◽  
Prediction Accuracy ◽  
Wind Farm ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Input Variables

This paper uses neural network combined with time series to establish rolling neural network model to predict short-term wind speed in the wind farm. In order to improve wind speed prediction accuracy, this paper analyzes effects of wind direction on wind speed by gray correlation analysis and obtains the correlation coefficient between wind speed at next moment and current wind direction is the largest by calculating. Then wind direction at current moment, historical wind speed and residuals which determined by time series are used as input variables to establish wind prediction model with rolling BP neural network. The simulation results show that neural network combined with time series which considers wind direction could improve the prediction accuracy when wind speed fluctuation is large.

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