scholarly journals Nonlinear multichannel singular spectrum analysis of the tropical Pacific climate variability using a neural network approach

2002 ◽  
Vol 107 (C7) ◽  
Author(s):  
William W. Hsieh
Keyword(s):  
Neural Network ◽  
Climate Variability ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Tropical Pacific ◽  
Network Approach ◽  
Neural Network Approach ◽  
Singular Spectrum ◽  
Pacific Climate Variability ◽  
The Tropical Pacific

scholarly journals Multistep Wind Speed Forecasting Using a Novel Model Hybridizing Singular Spectrum Analysis, Modified Intelligent Optimization, and Rolling Elman Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-21 ◽  
Author(s):  
Zhongshan Yang ◽  
Jian Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Hybrid Model ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Monitoring And Control ◽  
Elman Neural Network ◽  
Intelligent Optimization ◽  
Wind Speed Forecasting ◽  
Singular Spectrum

Wind speed high-accuracy forecasting, an important part of the electrical system monitoring and control, is of the essence to protect the safety of wind power utilization. However, the wind speed signals are always intermittent and intrinsic complexity; therefore, it is difficult to forecast them accurately. Many traditional wind speed forecasting studies have focused on single models, which leads to poor prediction accuracy. In this paper, a new hybrid model is proposed to overcome the shortcoming of single models by combining singular spectrum analysis, modified intelligent optimization, and the rolling Elman neural network. In this model, except for the multiple seasonal patterns used to reduce interferences from the original data, the rolling model is utilized to forecast the multistep wind speed. To verify the forecasting ability of the proposed hybrid model, 10 min and 60 min wind speed data from the province of Shandong, China, were proposed in this paper as the case study. Compared to the other models, the proposed hybrid model forecasts the wind speed with higher accuracy.

scholarly journals A hybrid model coupled with singular spectrum analysis for daily rainfall prediction

2010 ◽  
Vol 12 (4) ◽  
pp. 458-473 ◽  
Author(s):  
K. W. Chau ◽  
C. L. Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Regression ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Daily Rainfall ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Singular Spectrum ◽  
Artificial Neural

A hybrid model integrating artificial neural networks and support vector regression was developed for daily rainfall prediction. In the modeling process, singular spectrum analysis was first adopted to decompose the raw rainfall data. Fuzzy C-means clustering was then used to split the training set into three crisp subsets which may be associated with low-, medium- and high-intensity rainfall. Two local artificial neural network models were involved in training and predicting low- and medium-intensity subsets whereas a local support vector regression model was applied to the high-intensity subset. A conventional artificial neural network model was selected as the benchmark. The artificial neural network with the singular spectrum analysis was developed for the purpose of examining the singular spectrum analysis technique. The models were applied to two daily rainfall series from China at 1-day-, 2-day- and 3-day-ahead forecasting horizons. Results showed that the hybrid support vector regression model performed the best. The singular spectrum analysis model also exhibited considerable accuracy in rainfall forecasting. Also, two methods to filter reconstructed components of singular spectrum analysis, supervised and unsupervised approaches, were compared. The unsupervised method appeared more effective where nonlinear dependence between model inputs and output can be considered.

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