A New Hybrid-Multiscale SSA Prediction of Non-Stationary Time Series

Ghanbarzadeh, Mitra; Aminghafari, Mina

doi:10.1142/s021947751650005x

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…SSA is a typical time series analysis technique and is mainly utilized to solve the extraction of the trend or quasi‐periodic component, the reduction of signal noise, the prediction problem, and the detection of anomaly 31. At present, SSA has been widely used in climate, environment, finance, and other fields, which shows a very significant analysis effect 32–33…”

Section: Methodsmentioning

confidence: 99%

“…31 At present, SSA has been widely used in climate, environment, finance, and other fields, which shows a very significant analysis effect. 32,33 As a nonparametric spectral extraction method, SSA can be used to separate the mean trend and the fluctuation component from an original time series. The basic SSA algorithm consists of the two stages of decomposition and reconstruction, which is performed in the following steps.…”

Section: Singular Spectrum Analysismentioning

confidence: 99%

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Retracted: Forecasting model for wind power integrating least squares support vector machine, singular spectrum analysis, deep belief network, and locality‐sensitive hashing

et al. 2019

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Accurate wind power prediction can alleviate the negative influence on power system caused by the integration of wind farms into grid. In this paper, a novel combination model is proposed with the purpose of enhancing short-term wind power prediction precision. Singular spectrum analysis is utilized to decompose the original wind power series into the trend component and the fluctuation component. Then least squares support vector machine (LSSVM) is applied to forecast the trend component while deep belief network (DBN) is utilized to predict the fluctuation component. By this means, the performance advantages of LSSVM and DBN can be brought into full play. Moreover, the locality-sensitive hashing search algorithm is introduced to cluster the nearest training samples to further improve forecasting accuracy. Besides, the effect of LSSVM based on different kernel functions and the number of the nearest samples is investigated. The simulation results show that the normalized root mean square errors of the proposed model based on linear kernel function from1-step to 3-step forecasting are 2.13%, 5.03%, and 7.29%, respectively, which outperforms all the other comparison models. Therefore, it can be concluded that the proposed combination model provides a promising and effective alternative for short-term wind power prediction. KEYWORDS combination forecasting model, deep belief network, least squares support vector machine, locality-sensitive hashing, singular spectrum analysis, wind power

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Section: Methodsmentioning

confidence: 99%

Section: Singular Spectrum Analysismentioning

confidence: 99%

Retracted: Forecasting model for wind power integrating least squares support vector machine, singular spectrum analysis, deep belief network, and locality‐sensitive hashing

et al. 2019

View full text Add to dashboard Cite

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“…These decomposition methods can efficiently model the nonlinearities, however in many cases these components shows chaotic behaviour which degrades the prediction accuracy. To remove the uncertainty and low amplitude variations from these components and to improve the accuracy, singular spectrum analysis (SSA) [66,67] was found to be very useful. In [14], ensemble empirical mode decomposition (EEMD) is used to decomposed the time series data into different components.…”

Section: Hybrid Methodsmentioning

confidence: 99%

A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction

et al. 2022

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With large penetration of wind power into power grids, the accurate prediction of wind power generation is becoming extremely important. Planning, scheduling, maintenance, trading and smooth operations all depend on the accuracy of the prediction. However due to the highly non-stationary and chaotic behaviour of wind, accurate forecasting of wind power for different intervals of time becomes more challenging. Forecasting of wind power generation over different time spans is essential for different applications of wind energy. Recent development in this research field displays a wide spectrum of wind power prediction methods covering different prediction horizons. A detailed review of recent research achievements, performance, and information about possible future scope is presented in this article. This paper systematically reviews long term, short term and ultra short term wind power prediction methods. Each category of forecasting methods is further classified into four subclasses and a comparative analysis is presented. This study also provides discussions of recent development trends, performance analysis and future recommendations.

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“…Ghanbarzadeh and Aminghafari [46] presented a technique for predicting the time series of the nonstationary mode. It was on the basis of predicting nondecimated wavelet (NDW) signals via SSA, and then predicting the residuals making use of the wavelet regression.…”

Section: Introductionmentioning

confidence: 99%

Change Point Estimation of the Stationary State in Auto Regressive Moving Average Models, Using Maximum Likelihood Estimation and Singular Value Decomposition-based Filtering

Sheikhrabori

Aminnayeri

Ayoubi

2019

IJE

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In this paper, for the first time, the subject of change point estimation has been utilized in the stationary state of auto regressive moving average (ARMA) (1, 1). In the monitoring phase, in case the features of the question pursue a time series, i.e., ARMA(1,1), on the basis of the maximum likelihood technique, an approach will be developed for the estimation of the stationary state's change point. To estimate unidentified parameters following the change point, the dynamic linear model's filtering was utilized on the basis of the singular decomposition of values. The proposed model has wide applications in several fields such as finance, stock exchange marks and rapid production. The results of simulation showed the suggested estimator's effectiveness. In addition, a real example on stock exchange market is offered to delineate the application.

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A New Hybrid-Multiscale SSA Prediction of Non-Stationary Time Series

Cited by 9 publications

References 14 publications

Retracted: Forecasting model for wind power integrating least squares support vector machine, singular spectrum analysis, deep belief network, and locality‐sensitive hashing

Retracted: Forecasting model for wind power integrating least squares support vector machine, singular spectrum analysis, deep belief network, and locality‐sensitive hashing

A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction

Change Point Estimation of the Stationary State in Auto Regressive Moving Average Models, Using Maximum Likelihood Estimation and Singular Value Decomposition-based Filtering

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