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

Early studies have shown evidence of the seismo-ionospheric perturbations prior to large earthquakes. Due to dynamic complexity in the ionosphere, the identification of precursory ionospheric changes is quite challenging. In this study, we analyze the total electron content (TEC) in the global ionosphere map and investigate the TEC changes prior to M ≥ 6.0 earthquakes in the Chinese Mainland during 1998–2013 to identify possible seismo-ionospheric precursors. Singular spectrum analysis is applied to extract the trend and periodic variations including diurnal and semi-diurnal components, which are dominated by solar activities. The residual ΔTEC which is mainly composed of errors and possible perturbations induced by earthquakes and geomagnetic activities is further investigated, and the root-mean-square error is employed to detect anomalous changes. The F10.7 and Dst index is also used as criterion to rule out the anomalies when intense solar or geomagnetic activities occur. Our results are consistent with those of previous studies. It is confirmed that the negative anomalies are dominant 1–5 days before the earthquakes at the fixed point (35°N, 90°E) during 0600–1000 LT. The anomalies are more obvious near the epicenter area. The singular spectrum analysis method help to establish a more reliable variation background of TEC and thus may improve the identification of precursory ionospheric changes.

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

Sensors ◽

10.3390/s21041403 ◽

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.

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

Teorema Teori dan Riset Matematika ◽

10.25157/teorema.v5i1.3286 ◽

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.

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

Building Services Engineering Research and Technology ◽

10.1177/0143624418813434 ◽

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.

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Wave Signatures in Total Electron Content Variations: Filtering Problems

Remote Sensing ◽

10.3390/rs12081340 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1340 ◽

Cited By ~ 2

Author(s):

Boris Maletckii ◽

Yury Yasyukevich ◽

Artem Vesnin

Keyword(s):

Total Electron Content ◽

Moving Average ◽

Global Navigation Satellite Systems ◽

Analytical Models ◽

Mean Bias Error ◽

Bias Error ◽

Electron Content ◽

Type I ◽

Total Electron ◽

Order Polynomial

Over recent years, global navigation satellite systems (GNSSs) have been increasingly used to study near-Earth space. The basis for such studies is the total electron content (TEC) data. Standard procedures for detecting TEC wave signatures include variation selection and detrending. Our experience showed that the inaccurate procedure causes artifacts in datasets which might affect data interpretation, particularly in automated processing. We analyzed the features of various detrending and variation selection methods. We split the problem of the GNSS data filtering into two subproblems: detrending and variation selection. We examined centered moving average, centered moving median, 6th-order polynomial, Hodrick–Prescott filter, L1 filter, cubic smoothing spline, double-applied moving average for the GNSS-TEC detrending problem, and centered moving average, centered moving median, Butterworth filter, type I Chebyshev filter for the GNSS-TEC variation selection problem in this paper. We carried out the analysis based on both model and experimental data. Modeling was based on simple analytical models as well as the International Reference Ionosphere. Analysis of TEC variations of 2–10 min, 10–20 min, and 20–60 min under insufficient detrending conditions showed that the higher errors appear for the longer periods (20–60 min). For the detrending problem, the smoothing cubic spline provided the best results among the algorithms discussed in this paper. The spline detrending featured the minimal value of the mean bias error (MBE) and the root-mean-square error (RMSE), as well as high correlation coefficient. The 6th-order polynomial also produced good results. Spline detrending does not introduce a RMSE more than 0.25 TECU and MBE > 0.35 TECU for IRI trends, while, for the 6th-order polynomial, these errors can exceed 1 TECU in some cases. However, in 95% of observations the RMSE and MBE do not exceed 0.05 TECU. For the variation selection, the centered moving average filter showed the best performance among the algorithms discussed in this paper.

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Formal estimation of the random component in global maps of total electron content

Advances in Astronomy and Space Physics ◽

10.17721/2227-1481.6.56-60 ◽

2016 ◽

Vol 6 (1) ◽

pp. 56-60 ◽

Cited By ~ 1

Author(s):

V. Choliy

Keyword(s):

Total Electron Content ◽

Singular Spectrum Analysis ◽

Satellite System ◽

Electron Content ◽

Navigation Satellite ◽

Random Component ◽

Optimal Parameters ◽

Total Electron ◽

Global Navigation Satellite

Random component of the total electron content (TEC) maps, produced by global navigation satellite system processing centres, was analysed. Helmert transform (HT) and two-dimension singular spectrum analysis (2dSSA) were used. Optimal parameters (in the sense calculation speed versus quality) of the 2dSSA windows were determined along with precision estimations.

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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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