Investigation of Seasonal Movements in Time Series by a Variance Analysis Method

2020 ◽  
Vol 10 (10 (2)) ◽  
pp. 125-130
Author(s):  
Saadettin AYDIN
Keyword(s):  
Time Series ◽  
Variance Analysis ◽  
Analysis Method ◽  
Seasonal Movements

scholarly journals Statistical and Criticality Analysis of the Lower Ionosphere Prior to the 30 October 2020 Samos (Greece) Earthquake (M6.9), Based on VLF Electromagnetic Propagation Data as Recorded by a New VLF/LF Receiver Installed in Athens (Greece)

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 676
Author(s):  
Dimitrios Z. Politis ◽  
Stelios M. Potirakis ◽  
Yiannis F. Contoyiannis ◽  
Sagardweep Biswas ◽  
Sudipta Sasmal ◽  
...  
Keyword(s):  
Time Series ◽  
Fresnel Zone ◽  
Lower Ionosphere ◽  
Propagation Path ◽  
Northern Coast ◽  
Analysis Method ◽  
Ionospheric Propagation ◽  
Amplitude Data ◽  
Natural Time ◽  
Criticality Analysis

In this work we present the statistical and criticality analysis of the very low frequency (VLF) sub-ionospheric propagation data recorded by a VLF/LF radio receiver which has recently been established at the University of West Attica in Athens (Greece). We investigate a very recent, strong (M6.9), and shallow earthquake (EQ) that occurred on 30 October 2020, very close to the northern coast of the island of Samos (Greece). We focus on the reception data from two VLF transmitters, located in Turkey and Israel, on the basis that the EQ’s epicenter was located within or very close to the 5th Fresnel zone, respectively, of the corresponding sub-ionospheric propagation path. Firstly, we employed in our study the conventional analyses known as the nighttime fluctuation method (NFM) and the terminator time method (TTM), aiming to reveal any statistical anomalies prior to the EQ’s occurrence. These analyses revealed statistical anomalies in the studied sub-ionospheric propagation paths within ~2 weeks and a few days before the EQ’s occurrence. Secondly, we performed criticality analysis using two well-established complex systems’ time series analysis methods—the natural time (NT) analysis method, and the method of critical fluctuations (MCF). The NT analysis method was applied to the VLF propagation quantities of the NFM, revealing criticality indications over a period of ~2 weeks prior to the Samos EQ, whereas MCF was applied to the raw receiver amplitude data, uncovering the time excerpts of the analyzed time series that present criticality which were closest before the Samos EQ. Interestingly, power-law indications were also found shortly after the EQ’s occurrence. However, it is shown that these do not correspond to criticality related to EQ preparation processes. Finally, it is noted that no other complex space-sourced or geophysical phenomenon that could disturb the lower ionosphere did occur during the studied time period or close after, corroborating the view that our results prior to the Samos EQ are likely related to this mainshock.

C. PENGARUH UTANG LUAR NEGERI DAN EKSPOR TERHADAP PERTUMBUHAN EKONOMI (Studi pada Produk Domestik Bruto Indonesia Periode 2015-2019)

2021 ◽  
Vol 4 (1) ◽  
pp. 22
Author(s):  
Farah Syahri Maulidiyah
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Regression Analysis ◽  
Linear Regression Analysis ◽  
Foreign Debt ◽  
External Debt ◽  
Multiple Linear Regression Analysis ◽  
Analysis Method ◽  
Indonesian Government ◽  
Independent Variable

ABSTRACT The purpose of this research is to analyze the influence of exports and foreign debt which can affect Indonesia's GDP (Gross Domesty Product). The variables of this research are the foreign debt value of the Indonesian government and the value of Indonesian exports as the independent variable, and the value of Indonesia's GDP as the dependent variable. The data used are supporting data for the 2015-2019 period from the time series (time series) of Bank Indonesia and BPS. The data analysis method used multiple linear regression analysis. The results of this study are the value of the Indonesian government's foreign debt and the value of Indonesia's exports have a significant effect. Meanwhile, the results of the partial test (t-test) show that the value of foreign debt and exports of the Indonesian government greatly affects the value of Indonesia's GDP. Keywords : External Debt, Export, Economic Growth (Menggunakan template jurnal sinta 2 JESP (Jurnal Ekonomi dan Studi Pembangunan) eISSSN : 2502-7115 l pISSN : 2502-7115 Universitas Negeri Malang).

scholarly journals Prediction Methods for Routine Maintenance Costs of a Reinforced Concrete Beam Bridge Based on Panel Data

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaoli Shi ◽  
Bingbing Zhao ◽  
Yuling Yao ◽  
Feng Wang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Concrete Beam ◽  
Reinforced Concrete Beam ◽  
Maintenance Cost ◽  
Analysis Method ◽  
Series Analysis ◽  
The Cost ◽  
Beam Bridge ◽  
Physical Engineering

In order to make informed decisions on routine maintenance of bridges of expressways, the hierarchical regression analysis method was used to quantify factors influencing routine maintenance cost. Two calculation models for routine maintenance cost based on linear regression and time-series analysis were proposed. The results indicate that the logarithm of the historical routine maintenance cost is the dependent variable and the bridge age is the independent variable. The linear regression analysis was used to obtain a cost prediction model for routine maintenance of a beam bridge, which was combined with the quantity and price, and verified by a physical engineering example. In order to cope with the cost changes and future demands brought about by the emergence of new maintenance technologies, the time-series analysis method was used to obtain a model to predict the engineering quantities for the routine maintenance of a bridge based on standardized minor repair engineering quantities. Taking into account the actual cost of the minor repair project as well as the time-series analysis’ sample size demands, the annual engineering quantity was randomly decomposed into four quarterly data quantities, and the time-series analysis result was verified by physical engineering. These results can improve the calculation accuracy of the routine maintenance costs of reinforced concrete beam bridges. Furthermore, it can have a certain application value for improving the cost measurement module of bridge maintenance management systems.

Time Series Association State Analysis Method for Attacks on the Smart Internet of Electric Vehicle Charging Network

2019 ◽  
Vol 2673 (4) ◽  
pp. 217-228 ◽  
Author(s):  
Qianmu Li ◽  
Yinhai Wang ◽  
Ziyuan Pu ◽  
Shuo Wang ◽  
Weibin Zhang
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Electric Vehicle ◽  
The State ◽  
Analysis Method ◽  
Security Risks ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
Association State ◽  
State Analysis

A robust, integrated and flexible charging network is essential for the growth and deployment of electric vehicles (EVs). The State Grid of China has developed a Smart Internet of Electric Vehicle Charging Network (SIEN). At present, there are three main ways to attack SIEN maliciously: distributed data tampering; distributed denial of service (DDoS); and forged command attacks. Network attacks are random and continuous, closely related to time. By contrast, when analyzing the alarm in malicious attacks, the traditional Markov chain based model ignores the association relationship in the time series between states of alarm, so that the analysis and prediction of alarms are not suitable for real situations. This paper analyzes the characteristics of the three types of attack and proposes an association state analysis method on the time series. This method firstly analyzes alarm logs at different locations, different levels, and different types, and then establishes the temporal association of scattered and isolated alarm information. Secondly, it tracks the transition trend of abnormal events in the SIEN’s main station layer, the channel layer, and the sub-station layer. It also identifies the real attack behavior. This method not only provides a prediction of security risks, but, more importantly, it can also accurately analyze the trend of SIEN security risks. Compared with the ordinary Markov chain model, this method can better smooth the fluctuation of processing values, with higher real-time performance, stronger robustness, and higher precision. This method has been applied to the State Grid of China.

scholarly journals A Time–Frequency Correlation Analysis Method of Time Series Decomposition Derived from Synchrosqueezed S Transform

2019 ◽  
Vol 9 (4) ◽  
pp. 777 ◽  
Author(s):  
Gaoyuan Pan ◽  
Shunming Li ◽  
Yanqi Zhu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Time Varying ◽  
Analysis Method ◽  
Frequency Correlation ◽  
Time Frequency ◽  
Time Series Decomposition ◽  
S Transform ◽  
Stationary Signals ◽  
Correlation Analysis Method

Traditional correlation analysis is analyzed separately in the time domain or the frequency domain, which cannot reflect the time-varying and frequency-varying characteristics of non-stationary signals. Therefore, a time–frequency (TF) correlation analysis method of time series decomposition (TD) derived from synchrosqueezed S transform (SSST) is proposed in this paper. First, the two-dimensional time–frequency matrices of the signals is obtained by synchrosqueezed S transform. Second, time series decomposition is used to transform the matrices into the two-dimensional time–time matrices. Third, a correlation analysis of the local time characteristics is carried out, thus attaining the time–frequency correlation between the signals. Finally, the proposed method is validated by stationary and non-stationary signals simulation and is compared with the traditional correlation analysis method. The simulation results show that the traditional method can obtain the overall correlation between the signals but cannot reflect the local time and frequency correlations. In particular, the correlations of non-stationary signals cannot be accurately identified. The proposed method not only obtains the overall correlations between the signals, but can also accurately identifies the correlations between non-stationary signals, thus showing the time-varying and frequency-varying correlation characteristics. The proposed method is applied to the acoustic signal processing of an engine–gearbox test bench. The results show that the proposed method can effectively identify the time–frequency correlation between the signals.

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