STATISTICAL ANALYSIS AND PREDICTIVE MODELING OF EXTERNAL AND INTERNAL REINSURANCE MARKET STRUCTURES

2020 ◽  
Vol 11 (3) ◽  
pp. 167-175
Author(s):  
Vladimir D. Bogatyrev ◽  
Elena P. Rostova
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Predictive Modeling ◽  
Statistical Data ◽  
Time Series Models ◽  
Retrospective Data ◽  
Market Structures ◽  
Advantageous Position ◽  
The Russian Federation ◽  
Reinsurance Market

In the article the authors examine the reinsurance market of the Russian Federation; consider reinsurance premiums for incoming and outgoing external and internal reinsurance; based on statistical data, the authors made a conclusion about the externally oriented ceding market in the period 2013–2019. The authors present the structure of the reinsurance market by major companies and identify the main players in the market of incoming and outgoing reinsurance; consider the ratio of external and internal premiums for incoming and outgoing reinsurance. The authors complied time series models of reinsurance premiums for incoming and outgoing external and internal reinsurance based on retrospective data for 2016–2019. All functions are increasing, which indicates the positive dynamics of the studied market and the possibilities for further expansion and development. Based on the models, forecast values are calculated that allow to draw conclusions about the development and structure of the Russian reinsurance market. The reasons for the dominance of external reinsurance over internal in relation to outgoing contracts, consisting in the retroceding of risks to large international reinsurance companies, are identified, that occupy the most advantageous position in this market in comparison with domestic reinsurers. 

scholarly journals System for Forecasting COVID-19 Cases Using Time-Series and Neural Networks Models

2021 ◽  
Vol 5 (1) ◽  
pp. 46
Author(s):  
Mostafa Abotaleb ◽  
Tatiana Makarovskikh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Models ◽  
Third Wave ◽  
The Third ◽  
Modeling And Forecasting ◽  
Auto Regressive ◽  
The Russian Federation ◽  
R Programming

COVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling COVID-19 based on time-series models due to their accuracy in forecasting COVID-19 cases. We developed an “Epidemic. TA” system using R programming for modeling and forecasting COVID-19 cases. This system contains linear (ARIMA and Holt’s model) and non-linear (BATS, TBATS, and SIR) time-series models and neural network auto-regressive models (NNAR), which allows us to obtain the most accurate forecasts of infections, deaths, and vaccination cases. The second task is the implementation of our system to forecast the risk of the third wave of infections in the Russian Federation.

scholarly journals Monitoring High Throughput Distributed System using Statistical Data Analysis

2020 ◽  
Vol 8 (6) ◽  
pp. 4590-4596
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Distributed System ◽  
High Throughput ◽  
Statistical Data ◽  
Time Interval ◽  
Statistical Data Analysis ◽  
Cloud Data ◽  
Data Pipeline ◽  
Cloud Data Centers

Monitoring high throughput distributed system by using a statistical analysis of the “historical time series” of an Instrumentation Data”. “The Pipeline has been made to process the information which can be otherwise called data pipeline, is a lot of information handling components associated in arrangement, where yield of one component is the contribution of the next one”. Several codes are giving different visualization for statistical analysis of data. “Network and Cloud Data Centers” generate a lot of data every second; this data can be gathered as period arrangement information. A timeseries is a grouping taken at progressive similarly dispersed focuses in time that implies at a particular time interval to a particular time, the estimations of explicit information that was taken is known as information of a time-series. “This time-series information can be gathered utilizing framework measurements like CPU, Memory, and Disk utilization”. The TICK and ELK Stack is abbreviation for a foundation of open source instruments worked “to make collection, storage, graphing, and alerting” on time arrangement data incredibly easy. As an information collector, using Telegraf, “for storing and analyzing” information and the time-series database InfluxDB and Elasticsearch. For plotting and visualizing used Grafana and Kibana. Watchman is utilized for alert refinement and once system metrics usage exceeds the specified threshold, the alert is generated and sends it to the Telegram.

Statistical Analysis of Real Income of the Population of the Russian Federation

2020 ◽  
Vol 8 (3) ◽  
pp. 59-64
Author(s):  
I. Darymov ◽  
Aleksandr Kalyanov
Keyword(s):  
Statistical Analysis ◽  
Russian Federation ◽  
Statistical Data ◽  
Oil Prices ◽  
Real Income ◽  
Federal State ◽  
The World ◽  
Global Problems ◽  
The Russian Federation ◽  
Difficult Times

The article raises such an acute problem as the disproportionate change in population growth with rising inflation. The relevance of this problem cannot raise any questions. Sometimes people ask the question: “Why is there not enough money for food, although, relatively speaking, it was enough before?” The economy of our country is going through difficult times. The fall in world oil prices has an impact on the Russian ruble, which is especially important in connection with the latest developments in the world. These and other questions are devoted to issues related to the growth of the population of the Russian Federation, and the opportunity to buy them. The research base is the website of the Federal State Statistics Service, which should use personal observations and knowledge. In preparing the articles, methods of correlation analysis using group statistical data were applied and the regression model was built on their basis. The authors proposed ideas for solving global problems in the entire economic system of the Russian Federation.

Statistical analysis of a class of factor time series models

2006 ◽  
Vol 136 (7) ◽  
pp. 2367-2380
Author(s):  
Masanobu Taniguchi ◽  
Kousuke Maeda ◽  
Madan L. Puri
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Time Series Models

scholarly journals THE ROLE OF CUSTOMS AUTHORITIES IN THE SYSTEM OF MONITORING COMPLIANCE WITH THE CURRENCY LEGISLATION OF THE RUSSIAN FEDERATION

2020 ◽  
Vol 34 (2) ◽  
pp. 77-84
Author(s):  
Z.T. Abusufyanova ◽  
Keyword(s):  
Statistical Analysis ◽  
Russian Federation ◽  
System Analysis ◽  
Foreign Currency ◽  
Statistical Data ◽  
Economic Security ◽  
Synthesis System ◽  
Expert Assessments ◽  
The Russian Federation

Problems of illegal export of funds abroad are relevant for Russia. Offenses related to the non-return of exported foreign currency earnings to the country, transfers of foreign currency abroad under fictitious contracts for the supply of goods, smuggling of cash currency and other forms of illegal export of funds pose a real threat to the country's economic security. The article considers the main areas of currency control carried out by customs authorities, analyzes the activities of customs authorities in the implementation of currency control for the period from 2014 to 2018. When writing the work, the following research methods were used: expert assessments, analysis, synthesis, system analysis, statistical analysis, formal legal, comparative legal methods. Based on the analysis of statistical data of the Federal Customs Service of the Russian Federation, the opinions of scientists and experts in the field of customs, conclusions are drawn about the most common violations of the currency legislation of the Russian Federation identified by customs authorities.

scholarly journals Analysis of time series by the example of registration of variations in the gravitational field

2021 ◽  
Vol 43 (4) ◽  
pp. 76-90
Author(s):  
R.Z. Burtiev ◽  
Yu.V. Semenova ◽  
V.T. Kiriyak ◽  
E.V. Sidorenko ◽  
S.V. Troyan ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Data ◽  
Time Series Model ◽  
Statistical Dependence ◽  
Data Series ◽  
Time Series Models ◽  
Geophysical Processes ◽  
Gravimetric Data ◽  
Predicted Values

In this work, a time series model is used to study the structure of gravimetric data series to identify patterns in the change in the levels of the series and build its model in order to predict and study the relationships between the levels of gravimetric data. Observations of the activity of geophysical processes showed that the periods of variations in geophysical processes are scattered chaotically on the time axis. According to their schedule, it is impossible to definitely speak about the regularity in the duration of the periods of variations, and in the alternation of periods of seismic calm with a period of high seismic activity. The impetus for this study was the desire to analyze the structure of a number of formal methods to search for statistical patterns in the variations of geophysical parameters over time. Time series models were used to study the dynamics of geophysical events. Forecasting was carried out using the SPSS 20 package and EXCEL 2016. The accuracy of the forecast is indicated by the comparison of the forecast series with the actual data. The predicted values of the gravimetric data are within the confidence intervals. If you start forecasting too early, the forecast may differ from the forecast based on all statistical data. If the data shows seasonal trends, it is recommended to start forecasting from the date before the last point of the statistical data. Spatial and time series models can be used to study the dynamics of geophysical events. A spatial model describes a set of geophysical parameters at a given point in time. A time series is a series of regular observations of a certain parameter at successive points in time or at intervals of time. In this work, the time series model is used: to identify the statistical relationship between the frequency and depth of occurrence of earthquakes, as well as to identify the statistical dependence of these data on gravimetric variations; determination of patterns in the change in the levels of the series and the construction of its model in order to predict and study the relationships between geophysical phenomena.

Analysis of reliability of contact network devices on the Yekaterinburg-Chelyabinsk section

Innotrans ◽  
2021 ◽  
pp. 54-58
Author(s):  
Alexander V. Andryukov ◽  
◽  
Alexey A. Kovalev ◽  
Keyword(s):  
Statistical Analysis ◽  
Russian Federation ◽  
Statistical Data ◽  
Ice Formation ◽  
Contact Network ◽  
The South ◽  
Climatic Regions ◽  
The Russian Federation ◽  
Analysis Of Failures ◽  
Climatic Zoning

Damage to the contact network negatively affects the movement of trains: 70 % of failures lead to their delays. Breaks in the movement are more than an hour and in some cases reach three to four hours. The article analyzes reliability and failures of contact network devices on the South Ural Railway. The statistical data of failures of contact network devices from 2018 to 2019 are given, the nature of failures of technical means is determined. According to the map of climatic zoning, the share ratio of icy regions of the Russian Federation and branches of JSC «Russian Railways» is given. The sections of the Yekaterinburg - Chelyabinsk section are identified by climatic regions, which allows us to conduct a statistical analysis of failures in the selected sections associated with ice formation.

scholarly journals The software to conduct comparative analysis of dynamic series of medical statistical data.

2022 ◽  
pp. 92-94
Author(s):  
O. V. Perelomova ◽  
O. P. Gritsina ◽  
L. V. Trankovskaya
Keyword(s):  
Time Series ◽  
Comparative Analysis ◽  
Statistical Data ◽  
Physical Culture ◽  
Rights Protection ◽  
Consumer Rights ◽  
Research Organizations ◽  
Health Related ◽  
The Russian Federation ◽  
Dynamic Series

The software was designed to process and conduct comparative analysis of Big medical statistical data. This software is easy to use. It allows to conduct the analysis of medical statistical indexes in dynamics, gives a fair assessment of the time-series tendencies, helps to compare time-series of the studied indexes and presents graphic display of the information. The software can be used by health-care institutions, high education institutions, health-related scientific research organizations, physical culture organizations and institutions of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing of the Russian Federation.

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