scholarly journals A time series analysis of permanent GNSS stations in the northwest network of Iran

2021 ◽  
Vol 64 (2) ◽  
Author(s):  
Seyed Amin Ghasemi Khalkhali ◽  
Alireza A. Ardalan ◽  
Roohollah Karimi
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Deterministic Model ◽  
Noise Analysis ◽  
Model Analysis ◽  
Deformation Model ◽  
Series Analysis ◽  
Nonlinear Trend ◽  
Seismic Deformation ◽  
Periodic Components

The aim of this study is to estimate reliable velocities along with their realistic uncertainties based on a robust time series analysis including analysis of deterministic and stochastic (noise) models. In the deterministic model analysis part, we use a complete station motion model comprised of jump effects, linear and nonlinear trend, periodic components, and post-seismic deformation model. This part also consists of jump detection, outlier detection, and statistical significance of jumps. We perform the deterministic model analysis in an iterative process to elevate its efficiency. In the noise analysis part, first, we remove the spatial correlation of observations using the weighted stacking method based on the common mode error (CME) parameter. Next, a combination of white and flicker noises is used to determine the stochastic model. This time series analysis is applied for 11-year time series of 25 permanent GNSS stations from 2006 to 2016 in the northwest network of Iran. We reveal that there is a nonlinear trend in some stations, although most stations have a linear trend. In addition, we found that a combination of logarithmic and exponential functions is the most appropriate post-seismic deformation model in our study region. The result of the noise analysis shows that the spatial filtering reduces the norm of post-fit residual vector by 19.34%, 17.51%, and 12.44% on average for the east, north, and up components, respectively. Furthermore, the uncertainties obtained from the combination of white and flicker noises at the east, north, and up components are 5.0, 4.8, and 4.4 times greater than those of the white noise model, respectively. The results indicate that the stations move horizontally with an average velocity of 36.0 ± 0.3 mm/yr in the azimuth of 52.66° NE which is compatible with velocities obtained from MIDAS. We obtained the vertical velocity of most stations in the range of -5 to 5 mm/yr. However, in three stations of GGSH, ORYH, and BNAB, which are in the proximity of Lake Urmia, the vertical velocities are estimated to be -80.9 mm/yr, -50.6 mm/yr, and -11.4 mm/yr, respectively. Moreover, we found that these three stations possess large periodic signal amplitudes in all three coordinate components as well as a nonlinear trend in the up component.

Time series analysis of Holocene climate data

1990 ◽  
Vol 330 (1615) ◽  
pp. 601-616 ◽  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Domain ◽  
Small Sample ◽  
Climate Data ◽  
Holocene Climate ◽  
Series Analysis ◽  
Window Method ◽  
Band Limited ◽  
Periodic Components

Holocene climate records are imperfect proxies for processes containing complicated mixtures of periodic and random signals. I summarize time series analysis methods for such data with emphasis on the multiple-data-window technique. This method differs from conventional approaches to time series analysis in that a set of data tapers is applied to the data in the time domain before Fourier transforming. The tapers, or data windows, are discrete prolate spheroidal sequences characterized as being the most nearly band-limited functions possible among functions defined on a finite time domain. The multiple-window method is a small-sample theory and essentially an inverse method applied to the finite Fourier transform. For climate data it has the major advantage of providing a narrowband F -test for the presence and significance of periodic components and of being able to separate them from the non-deterministic part of the process. Confidence intervals for the estimated quantities are found by jackknifing across windows. Applied to 14 C records, this method confirms the presence of the ‘Suess wiggles’ and give an estimated period of 208.2 years. Analysis of the thickness variations of bristlecone pine growth rings shows a general absence of direct periodic components but a variation in the structure of the time series with a 2360-year period.

scholarly journals INTERFEROMETRIC POINT TARGET ANALYSIS (IPTA) FOR LANDSLIDE MONITORING

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 1079-1083
Author(s):  
M. Yarmohammad Touski ◽  
M. Veiskarami ◽  
M. Dehghani
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
High Performance ◽  
Landslide Monitoring ◽  
Deformation Model ◽  
Persistent Scatterer Interferometry ◽  
High Deformation ◽  
Target Analysis ◽  
Series Analysis ◽  
Point Target

Abstract. By the advent of Persistent Scatterer Interferometry (PSI) time-series analysis, this technique has demonstrated high performance in producing accurate measurements of ground displacements. However, due to several limitations such as high deformation rate, lack of man-made features and rough topographic characteristic, the efficiency of the PSI significantly decreases. The main goal of this paper is to illustrate the potential of one of the PSI methods namely the Interferometric Point Target Analysis (IPTA) to measure the deformation caused by landslide. The landslide occurs in an area lacking man-made features with rough topography. To this end, 28 Sentinel-1A SLC images spanning from October 14 2014 to October 27 2016, were used to generate single-master interferograms. The PS pixels were identified using amplitude dispersion and spectral diversity criteria. The PS pixels were unwrapped considering a linear model for the deformation behavior in an iterative manner. The residual topography and atmospheric contributions were estimated in each iteration and subtracted from the PS pixels phases. The results were finally compared to those extracted from conventional Small Baseline Subset (SBAS) time series analysis applied on the same datasets. A good agreement existed between two methods in some locations whereas unwrapping errors probably due to improper deformation model were observed in a couple of points.

scholarly journals Intervention Model Analysis The Number of Domestic Passengers at Sultan Hasanuddin Airports

2021 ◽  
Vol 1 (1) ◽  
pp. 41-49
Author(s):  
Andi Ferosita Sustrisno ◽  
Rais ◽  
Iman Setiawan
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Natural Disasters ◽  
Model Analysis ◽  
Intervention Analysis ◽  
Intervention Model ◽  
Model Data ◽  
Transportation Services ◽  
Series Analysis ◽  
Short Period

Domestic passengers are objects whose travel / flight transportation services only cover the domestic area. The increase or decrease in the number of domestic passengers is usually influenced by the occurrence of intervention. This research uses the intervention analysis. Intervention analysis is the time series analysis to model data that is determined by the presence of an intervention. Intervention analysis is one of the time series analysis to model data that are affected by the occurrence of a particular event in a short period of time, such as accidents, natural disasters, and promotions. This research is used to establish intervention model with pulse function of passengers of domestic Sultan Hasanuddin Airport. The result of the research were obtained the model Seasonal ARIMA .There were 6 intervention times during 2006 - 2018, by entering the intervention order b = 0, s = 0, and r = 1 based on the smallest AIC value is -303,66 with MAPE value is 6,1023. Keywords : Domestic passanger, Seasonal ARIMA, MAPE, Intervention analysis, pulse function

Dynamical Peculiarities of Concatenational Changes in Functional Cardiovascular Parameters

2018 ◽  
Vol 3 (82) ◽  
Author(s):  
Eurelija Venskaitytė ◽  
Jonas Poderys ◽  
Tadas Česnaitis
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Functional State ◽  
The Body ◽  
Functional Changes ◽  
High Intensity Training ◽  
Series Analysis ◽  
Greco Roman ◽  
St Segment ◽  
Standard Lead

Research  background  and  hypothesis.  Traditional  time  series  analysis  techniques,  which  are  also  used  for the analysis of cardiovascular signals, do not reveal the relationship between the  changes in the indices recorded associated with the multiscale and chaotic structure of the tested object, which allows establishing short-and long-term structural and functional changes.Research aim was to reveal the dynamical peculiarities of interactions of cardiovascular system indices while evaluating the functional state of track-and-field athletes and Greco-Roman wrestlers.Research methods. Twenty two subjects participated in the study, their average age of 23.5 ± 1.7 years. During the study standard 12 lead electrocardiograms (ECG) were recorded. The following ECG parameters were used in the study: duration of RR interval taken from the II standard lead, duration of QRS complex, duration of JT interval and amplitude of ST segment taken from the V standard lead.Research  results.  Significant  differences  were  found  between  inter-parametric  connections  of  ST  segment amplitude and JT interval duration at the pre and post-training testing. Observed changes at different hierarchical levels of the body systems revealed inadequate cardiac metabolic processes, leading to changes in the metabolic rate of the myocardium and reflected in the dynamics of all investigated interactions.Discussion and conclusions. It has been found that peculiarities of the interactions of ECG indices interactions show the exposure of the  functional changes in the body at the onset of the workload. The alterations of the functional state of the body and the signs of fatigue, after athletes performed two high intensity training sessions per day, can be assessed using the approach of the evaluation of interactions between functional variables. Therefore the evaluation of the interactions of physiological signals by using time series analysis methods is suitable for the observation of these processes and the functional state of the body.Keywords: electrocardiogram, time series, functional state.

The Contribution of Foreign Direct Investment for Economic Growth of Ethiopia: Time Series Analysis

2018 ◽  
Vol 9 (02) ◽  
Author(s):  
Addissie Melak
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Foreign Direct Investment ◽  
Time Series Analysis ◽  
Direct Investment ◽  
Policy Framework ◽  
Annual Growth Rate ◽  
African Countries ◽  
Series Analysis ◽  
Integration Test

Economic growth of countries is one of the fundamental questions in economics. Most African countries are opening their economies for welcoming of foreign investors. As such Ethiopia, like many African countries took measures to attract and improve foreign direct investment. The purpose of this study is to examine the contribution of foreign direct investment (FDI) for economic growth of Ethiopia over the period of 1981-2013. The study shows an overview of Ethiopian economy and investment environment by the help of descriptive and econometric methods of analysis to establish empirical investigation for the contribution of FDI on Ethiopian economy. OLS method of time series analysis is employed to analyse the data. The stationary of the variables have been checked by using Augmented Dickey Fuller (ADF) Unit Root test and hence they are stationery at first difference. The co- integration test also shows that there is a long run relationship between the dependent and independent variables. Accordingly, the finding of the study shows that FDI, GDP per capita, exchange rate, total investment as percentage of GDP, inflow of FDI stock, trade as percentage of GDP, annual growth rate of GDP and liberalization of the economy have positive impact on Ethiopian GDP. Whereas Gross fixed domestic investment, inflows of FDI and Gross capital formation influence economic growth of Ethiopia negatively. This finding suggests that there should be better policy framework to attract and improve the volume of FDI through creating conducive environment for investment.

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