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.

scholarly journals Analysis and prediction of polar motion using MSSA method

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Polar Motion ◽  
Rotation Axis ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Autoregressive Moving Average ◽  
Rotational Axis ◽  
Autoregressive Moving Average Model ◽  
System Service ◽  
Moving Average Model ◽  
Polar Motion Prediction

AbstractPolar motion is the movement of the Earth's rotational axis relative to its crust, reflecting the influence of the material exchange and mass redistribution of each layer of the Earth on the Earth's rotation axis. To better analyze the temporally varying characteristics of polar motion, multi-channel singular spectrum analysis (MSSA) was used to analyze the EOP 14 C04 series released by the International Earth Rotation and Reference System Service (IERS) from 1962 to 2020, and the amplitude of the Chandler wobbles were found to fluctuate between 20 and 200 mas and decrease significantly over the last 20 years. The amplitude of annual oscillation fluctuated between 60 and 120 mas, and the long-term trend was 3.72 mas/year, moving towards N56.79 °W. To improve prediction of polar motion, the MSSA method combining linear model and autoregressive moving average model was used to predict polar motion with ahead 1 year, repeatedly. Comparing to predictions of IERS Bulletin A, the results show that the proposed method can effectively predict polar motion, and the improvement rates of polar motion prediction for 365 days into the future were approximately 50% on average.

scholarly journals FORECASTING DATA RUNTUN WAKTU MUSIMAN MENGGUNAKAN METODE SINGULAR SPECTRUM ANALYSIS (SSA)

2020 ◽  
Vol 5 (1) ◽  
pp. 47
Author(s):  
Dadang Ruhiat ◽  
Dini Andiani ◽  
Wulan Nurul Kamilah
Keyword(s):  
Spectrum Analysis ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Forecasting Model ◽  
Autoregressive Integrated Moving Average ◽  
Absolute Percentage Error ◽  
Singular Spectrum

Pemodelan dan forecasting data runtun waktu akhir-akhir ini terus berkembang dan digunakan di berbagai bidang termasuk di bidang hidrologi. Parameter hidrologi yang sangat penting adalah debit sungai di Indonesia sebagaimana halnya di negara tropis lainnya, besaran dan fluktuasinya dipengaruhi oleh dua faktor musiman, yaitu musim hujan dan kemarau. Pemodelan dan forecasting runtun waktu berbasis statistik pada dasarnya dapat dilakukan melalui dua pendekatan, yaitu statistik parametrik dan statistik non-parametrik. Namun fakta menunjukkan pemodelan dan forecasting runtun waktu melalui pendekatan statistik parametrik  lebih banyak dilakukan. Pada penelitian ini dilakukan pemodelan dan forecasting data runtun waktu debit sungai melalui pendekatan statistik non-parametrik dengan menggunakan metode Singular Spectrum Analysis (SSA).  Tujuan dari penelitian ini adalah untuk mengetahui hasil forecasting metode SSA dan mengetahui hasil komparasinya dengan hasil forecasting statistik parametrik yang telah dilakukan sebelumnya melalui model Seasonal Autoregressive Integrated Moving Average (SARIMA). Metode proses penelitian ini adalah berupa kajian teori yang kemudian dilanjutkan dengan proses komputasi. Hasil analisis menunjukkan metode SSA memberikan hasil forecasting dengan nilai Mean Absolute Percentage Error (MAPE) yang  lebih kecil dari model SARIMA. Dengan demikian disimpulkan forecasting runtun waktu debit sungai yang berpola musiman melalui metode SSA relatif lebih baik dari hasil forecasting model SARIMA.

Total electron content prediction using singular spectrum analysis and autoregressive moving average approach

2022 ◽  
Vol 367 (1) ◽  
Author(s):  
J. R. K. Kumar Dabbakuti ◽  
Mallika Yarrakula ◽  
Sampad Kumar Panda ◽  
Punyawi Jamjareegulgarn ◽  
Mohd Anul Haq
Keyword(s):  
Total Electron Content ◽  
Spectrum Analysis ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Autoregressive Moving Average ◽  
Electron Content ◽  
Total Electron ◽  
Singular Spectrum

A benchmark and real-time estimator for the passenger arrival rate to elevator system

2018 ◽  
Vol 40 (2) ◽  
pp. 135-150
Author(s):  
Ahmad Hammoudeh ◽  
Lutfi Al-Sharif ◽  
Mohammad Al-Shabi
Keyword(s):  
Real Time ◽  
Linear Model ◽  
Kalman Filtering ◽  
Spectrum Analysis ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Arrival Rate ◽  
Singular Spectrum ◽  
Group Control ◽  
Weighted Moving Average

Arrival rate is the number of passengers arriving for elevator service in a certain period of time. Arrival rate is fundamental in expressing the heaviness of the traffic. Hence, it is vital for determining the required number of elevators and the specifications of each elevator such as the speed, capacity, and sector sizes. The passenger arrival process is a random process that is full of noise, and a processing step is required to extract the arrival rate from recorded arrival times of passengers. This work develops a real-time estimator and a benchmark for estimating the arrival rate. There are three contributions in this work; the first is suggesting a benchmark for estimating arrival rate; singular spectrum analysis extracts the arrival rate from noisy data. Hence, singular spectrum analysis is suggested as a benchmark for evaluating the performance of other algorithms. Even though singular spectrum analysis is powerful in extracting the arrival rate, it is not convenient for updating the arrival rate in real time. The second contribution is developing a real-time estimator for the passenger arrival rate that updates its parameters dynamically; dynamic exponentially weighted moving average was developed to provide instantaneous arrival rate updates. The third contribution is introducing exponentially weighted moving average as a linear model for passenger arrival, which opens the door to a large number of model-based algorithms in control theory; Kalman filtering was developed in this work on the top of the EWMA linear model. The results of applying Kalman filtering and DEWMA to real-life data show them as efficient methods for estimating passenger arrival rate to the elevators in real time. Practical application: The methods presented in this paper would allow an elevator controller designer to detect the intensity of the passenger arrival rate. By doing this, it is possible for the elevator controller to switch between different group control algorithms. For example, it could decide to switch from conventional group control to sectoring control and vice versa.

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

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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