scholarly journals Forecasting of Air Maximum Temperature on Monthly Basis Using Singular Spectrum Analysis and Linear Autoregressive Model

2021 ◽  
Vol 877 (1) ◽  
pp. 012033
Author(s):  
Nabeel Saleem Saad Al-Bdairi ◽  
Salah L. Zubaidi ◽  
Hussein Al-Bugharbee ◽  
Khalid Hashim ◽  
Sabeeh L. Farhan ◽  
...  
Keyword(s):  
Air Temperature ◽  
Spectrum Analysis ◽  
Autoregressive Model ◽  
Fitness Function ◽  
Singular Spectrum Analysis ◽  
Maximum Temperature ◽  
Monthly Basis ◽  
Singular Spectrum ◽  
Prediction And Forecasting ◽  
Maximum Air Temperature

Abstract In this research, the singular spectrum analysis technique is combined with a linear autoregressive model for the purpose of prediction and forecasting of monthly maximum air temperature. The temperature time series is decomposed into three components and the trend component is subjected for modelling. The performance of modelling for both prediction and forecasting is evaluated via various model fitness function. The results show that the current method presents an excellent performance in expecting the maximum air temperature in future based on previous recordings.

scholarly journals A Hybrid Short-Term Power Load Forecasting Model Based on the Singular Spectrum Analysis and Autoregressive Model

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hongze Li ◽  
Liuyang Cui ◽  
Sen Guo
Keyword(s):  
Spectrum Analysis ◽  
Electricity Market ◽  
Autoregressive Model ◽  
Singular Spectrum Analysis ◽  
Load Forecasting ◽  
Ar Model ◽  
Short Term ◽  
Singular Spectrum ◽  
Power Load ◽  
Power Load Forecasting

Short-term power load forecasting is one of the most important issues in the economic and reliable operation of electricity power system. Taking the characteristics of randomness, tendency, and periodicity of short-term power load into account, a new method (SSA-AR model) which combines the univariate singular spectrum analysis and autoregressive model is proposed. Firstly, the singular spectrum analysis (SSA) is employed to decompose and reconstruct the original power load series. Secondly, the autoregressive (AR) model is used to forecast based on the reconstructed power load series. The employed data is the hourly power load series of the Mid-Atlantic region in PJM electricity market. Empirical analysis result shows that, compared with the single autoregressive model (AR), SSA-based linear recurrent method (SSA-LRF), and BPNN (backpropagation neural network) model, the proposed SSA-AR method has a better performance in terms of short-term power load forecasting.

scholarly journals ANÁLISE DE SÉRIES TEMPORAIS DA TEMPERATURA DO AR E UMIDADE RELATIVA EM CUIABÁ ATRAVÉS DO MÉTODO DE ANÁLISE DE ESPECTRO SINGULAR (ANALYSIS OF TIME SERIES OF AIR TEMPERATURE AND RELATIVE HUMIDITY IN CUIABÁ THROUGH THE METHOD OF ANALYSIS OF SINGULAR SPECTRUM)

2016 ◽  
Vol 19 ◽  
Author(s):  
Hozana Silva Ferreira ◽  
Sérgio Roberto de Paulo ◽  
Iramaia Jorge Cabral de Paulo ◽  
Anna Carolinna Albino Santos ◽  
Raphael De Souza Rosa Gomes
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Air Temperature ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Singular Analysis ◽  
Analysis Of Time Series

Nos últimos anos o método de Análise de Espectro Singular (SSA- Singular Spectrum Analysis) tem sido utilizado como uma técnica na análise de séries temporais de dados meteorológicos e climáticos, possibilitando extrair informações úteis a partir de séries temporais estudadas. Nessa pesquisa, essa técnica foi utilizada no estudo das variáveis de temperatura do ar e umidade relativa em Cuiabá dos anos de 2000 a 2013, utilizando dados fornecidos pelo Instituto Nacional de Meteorologia (INMET).  O foco principal do estudo foi extrair informações sobre as tendências e efeitos sazonais das séries temporais. A ação conjunta desses dois fatores é particularmente evidente no caso da temperatura, que apresenta, comumente, dois máximos anuais.  

scholarly journals Multi-Channel Singular Spectrum Analysis on Geocenter Motion and Its Precise Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1403
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Solid Earth ◽  
Spectrum Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Autoregressive Moving Average Model ◽  
Seasonal Characteristics ◽  
Geocenter Motion ◽  
Moving Average Model

Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.

Application of singular spectrum analysis in reconstruction of the annual signal from GRACE

2020 ◽  
Vol 14 (3) ◽  
pp. 295-302
Author(s):  
Chuandong Zhu ◽  
Wei Zhan ◽  
Jinzhao Liu ◽  
Ming Chen
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Mixture Effect ◽  
Singular Spectrum ◽  
Grace Data ◽  
Terrestrial Water ◽  
Annual Signal ◽  
Long Term Variations

AbstractThe mixture effect of the long-term variations is a main challenge in single channel singular spectrum analysis (SSA) for the reconstruction of the annual signal from GRACE data. In this paper, a nonlinear long-term variations deduction method is used to improve the accuracy of annual signal reconstructed from GRACE data using SSA. Our method can identify and eliminate the nonlinear long-term variations of the equivalent water height time series recovered from GRACE. Therefore the mixture effect of the long-term variations can be avoided in the annual modes of SSA. For the global terrestrial water recovered from GRACE, the peak to peak value of the annual signal is between 1.4 cm and 126.9 cm, with an average of 11.7 cm. After the long-term and the annual term have been deducted, the standard deviation of residual time series is between 0.9 cm and 9.9 cm, with an average of 2.1 cm. Compared with the traditional least squares fitting method, our method can reflect the dynamic change of the annual signal in global terrestrial water, more accurately with an uncertainty of between 0.3 cm and 2.9 cm.

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