One hybrid model combining singular spectrum analysis and LS + ARMA for polar motion prediction

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.

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Short-Term Traffic Flow Prediction of Expressway: A Hybrid Method Based on Singular Spectrum Analysis Decomposition

Advances in Civil Engineering ◽

10.1155/2021/4313970 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Chunyan Shuai ◽

Zhengyang Pan ◽

Lun Gao ◽

HongWu Zuo

Keyword(s):

Traffic Flow ◽

Hybrid Model ◽

Spectrum Analysis ◽

Short Term Memory ◽

Singular Spectrum Analysis ◽

Support Vector ◽

Short Term ◽

Traffic Flow Prediction ◽

Singular Spectrum ◽

Flow Prediction

Real-time expressway traffic flow prediction is always an important research field of intelligent transportation, which is conducive to inducing and managing traffic flow in case of congestion. According to the characteristics of the traffic flow, this paper proposes a hybrid model, SSA-LSTM-SVR, to improve forecasting accuracy of the short-term traffic flow. Singular Spectrum Analysis (SSA) decomposes the traffic flow into one principle component and three random components, and then in terms of different characteristics of these components, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) are applied to make prediction of different components, respectively. By fusing respective forecast results, SSA-LSTM-SVR obtains the final short-term predictive value. Experiments on the traffic flows of Guizhou expressway in January 2016 show that the proposed SSA-LSTM-SVR model has lower predictive errors and a higher accuracy and fitting goodness than other baselines. This illustrates that a hybrid model for traffic flow prediction based on components decomposition is more effective than a single model, since it can capture the main regularity and random variations of traffic flow.

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A Hybrid Model for Forecasting Groundwater Levels Based on Fuzzy C-Mean Clustering and Singular Spectrum Analysis

Water ◽

10.3390/w9070541 ◽

2017 ◽

Vol 9 (7) ◽

pp. 541 ◽

Cited By ~ 4

Author(s):

◽

Keyword(s):

Hybrid Model ◽

Spectrum Analysis ◽

Singular Spectrum Analysis ◽

Groundwater Levels ◽

Singular Spectrum

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A Hybrid Model Integrating Singular Spectrum Analysis and Backpropagation Neural Network for Stock Price Forecasting

Revue d intelligence artificielle ◽

10.18280/ria.350606 ◽

2021 ◽

Vol 35 (6) ◽

pp. 483-488

Author(s):

Asmaa Y. Fathi ◽

Ihab A. El-Khodary ◽

Muhammad Saafan

Keyword(s):

Neural Network ◽

Hybrid Model ◽

Spectrum Analysis ◽

Stock Prices ◽

Stock Markets ◽

Stock Price ◽

Singular Spectrum Analysis ◽

Predictive Performance ◽

Backpropagation Neural Network ◽

Singular Spectrum

The primary purpose of trading in stock markets is to profit from buying and selling listed stocks. However, numerous factors can influence the stock prices, such as the company's present financial situation, news, rumor, macroeconomics, psychological, economic, political, and geopolitical factors. Consequently, tremendous challenges already exist in predicting noisy stock prices. This paper proposes a hybrid model integrating the singular spectrum analysis (SSA) and the backpropagation neural network (BPNN) to forecast daily closing prices in stock markets. The model first decomposes the stock prices into several components using the SSA. Then, the extracted components are utilized for training BPNNs to forecast future prices. Compared with the BPNN, the hybrid SSA-BPNN model demonstrates a better predictive performance, indicating the SSA's ability to extract hidden information and reduce the noise effect of the original time series.

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Inflation Forecasting for East Kalimantan Province Using Hybrid Singular Spectrum Analysis- Autoregressive Integrated Moving Average Model

Jurnal Matematika Statistika dan Komputasi ◽

10.20956/j.v18i1.14284 ◽

2021 ◽

Vol 18 (1) ◽

pp. 78-92

Author(s):

Melisa Arumsari ◽

Sri Wahyuningsih ◽

Meiliyani Siringoringo

Keyword(s):

Hybrid Model ◽

Spectrum Analysis ◽

Moving Average ◽

Singular Spectrum Analysis ◽

Percentage Error ◽

Forecasting Accuracy ◽

Autoregressive Integrated Moving Average ◽

Singular Spectrum ◽

East Kalimantan ◽

Inflation Forecasting

The Singular Spectrum Analysis (SSA)-Autoregressive Integrated Moving Average (ARIMA) hybrid method is a good combination of forecasting methods to improve forecasting accuracy and is suitable for economic data that tends to have trend and seasonal patterns, one of which is inflation data. The purpose of this study is to obtain the results of inflation forecasting for East Kalimantan Province in 2021 using the SSA-ARIMA hybrid model. The results of the inflation forecasting for East Kalimantan Province in 2021 using the SSA-ARIMA(1,1,1) hybrid model overall experienced an increase and the highest inflation in 2021 occurred in December of 0.92% with a forecasting accuracy level based on the Root Mean Square Error (RMSE) was 0.069399 and Mean Absolute Percentage Error (MAPE) was 32.61084%

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