Visualization challenge on time series statistical data

2017 ◽  
Author(s):  
Yukari Shirota ◽  
Takako Hashimoto ◽  
Basabi Chakraborty
Keyword(s):  
Time Series ◽  
Statistical Data

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.

scholarly journals Credit Cycles: Econometric Analysis and Evidence for Russia

2014 ◽  
pp. 113-122
Author(s):  
N. V. Artamonov ◽  
D. V. Artamonov ◽  
V. A. Artamonov
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Interest Rates ◽  
Russian Federation ◽  
Statistical Data ◽  
Econometric Analysis ◽  
Short Term ◽  
Credit Cycles ◽  
Real Gdp

One of the principal problem in contemporary macroeconomics is concerned with factors increasing or decreasing economic dynamics. The mainstream approach is based on neoclassical assumptions, but recently new approaches appear mostly based on new Keynesian concepts. In present time the influence of monetary market and credit instruments become more and more significant. Credit resources of banking and financial structures can affect and distort to reallocation of resources for national and even for global economic. In present paper an empiric and econometric analysis for some macroeconometric and monetary indices for Russian Federation is done. An econometrical models describing the influence of credit variables onto real GDP is estimated. It is shown that in short-term periods changes in credit variables do influence significantly onto GDP. It is shown that on short-term periods changes in money aggregate M2 brings influence (through credit variables) onto national output. As well it is shown that changes in short-term interest rate brings significant negative influence onto real output. Impulse response functions for GDP on shocks of credit variables, monetary base and short-term interest rate are evaluated. For the present study of credit cycles and their impact to real business cycles statistical data (quarterly time series) on the following factors for Russian Federation are collected: nominal and real GDP, monetary base M2, short-term interest rate, long-term interest rate (10-year treasuries bill rate), total debt outstanding. All time series are seasonally adjusted and collected for the period 2004 Q1 - 2013 Q2. All interest rates are adjusted for inflation (i.e. we deal with real interest rates). The investigation of long-term relationship for the factors under consideration are based on integration. It is important to note that in the present paper all econometric models are estimated on "pure" statistical data, while in many research papers on business and credit cycles all evaluations and inferences are based on "filtered" time series (mostly filtered by Hodrick-Prescott's method). In present paper "causality" always means "Granger causality". All estimations are made in gretl, an open-source multiplatform econometric software.

scholarly journals The relationship between M3 and consumer price index in the Czech Republic

2018 ◽  
Vol 4 (10) ◽  
pp. 226-236
Author(s):  
Libena Cernohorska
Keyword(s):  
Time Series ◽  
Czech Republic ◽  
Price Index ◽  
Statistical Data ◽  
Consumer Price Index ◽  
Monetary Aggregate ◽  
The Czech Republic ◽  
Significance Level ◽  
The Relationship ◽  
Cointegration Relationship

This paper aimed at analysing the influence of monetary aggregate M3 on consumer price index (CPI) in the Czech Republic. Cointegrating this selected indicator M3 is demonstrated in relation to the development of CPI using the Engle – Granger cointegration test. These tests are applied to selected statistical data from the years 2003 to 2016. After using the Akaike criteria for all-time series, we analysed a unit root using the Dickey–Fuller test. If the time series are non-stacionary, testing is then continued with the Engle–Granger test to detect cointegration relations. Based on these tests, it is found that at a significance level of 0.05, a cointegration relationship between M3 and CPI in the Czech Republic does not exist. Conclusions resulting from the verification of the hypotheses are supported with graphical visualisation of data from which it is apparent that these hypotheses can be rejected. Keywords: M3; Czech Republic ; CPI ; Akaike criteria

IMPACT OF VACCINATION ON THE COVID-19 PANDEMIC: BIBLIOMETRIC ANALYSIS AND CROSS COUNTRY FORECASTING BY FOURIER SERIES

2021 ◽  
Vol 74 (10) ◽  
pp. 2359-2367
Author(s):  
Olha V. Kuzmenko ◽  
Vladyslav A. Smiianov ◽  
Lesia A. Rudenko ◽  
Mariia O. Kashcha ◽  
Tetyana A. Vasilyeva ◽  
...  
Keyword(s):  
Time Series ◽  
Incidence Rate ◽  
United States Of America ◽  
Statistical Data ◽  
Self Organization ◽  
Disease Course ◽  
Cross Country ◽  
Predicted Values ◽  
Cyclic Models ◽  
The Impact

The aim: Is to build a forecast of the COVID-19 disease course, considering the vaccination of the population from particular countries. Materials and methods: Based on the analysis of statistical data, the article deals with the topical issue of the impact made by vaccination on the prevention of the COVID-19 pandemic. The time series, showing the dynamics of changes in the number of infected in Chile, Latvia, Japan, Israel, Australia, Finland, India, United States of America, New Zealand, Czech Republic, Venezuela, Poland, Ukraine, Brazil, Georgia for the period 07.08. 2020–09.09.2021, are analyzed. Trend-cyclic models of time series are obtained using fast Fourier transform. The predicted values of the COVID-19 incidence rate for different countries in the period from September 10, 2021 to February 2, 2022 were calculated using the constructed models. Results and conclusions: The results of the study show that vaccination of the population is one of the most effective methods to prevent the COVID-19 pandemic. The proposed method of modeling the dynamics of the incidence rate based on statistical data can be used to build further predictions of the incidence rate dynamics. The study of behavioral aspects of trust in vaccination is proposed to be conducted within the theory regarding the self-organization of complex systems.

Quantitative Methods in Transformation Research

2019 ◽  
pp. 191-200
Author(s):  
Gert Pickel ◽  
Susanne Pickel
Keyword(s):  
Time Series ◽  
Survey Data ◽  
Quantitative Data ◽  
Quantitative Methods ◽  
Statistical Data ◽  
Political Attitudes ◽  
Future Research ◽  
Multilevel Analyses ◽  
Micro Data ◽  
Representative Samples

Quantitative methods used in research on transformation are based on categorizations and translation of information into figures. We can distinguish analyses of basic statistical data and survey data, which use representative samples to characterize populations. Quantitative macro analyses of measures of democracy, and analyses of quantitative micro data of political attitudes, provide the most important findings for transformation research. For future research, it is desirable to expand existing time series (providing macro and micro quantitative data), and to analyse interdependencies (in a chronologically comparative perspective, too). A convergence of approaches that are influenced by area research and macro approaches would also be useful. An increase in use of multilevel analyses and multi-method designs may support this development. It would also highlight the capacity of the field and cases covered by transformation research—namely, the greater variance of outputs and outcomes within transformation countries compared to the variance within Organisation for Economic Co-operation and Development countries.

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 Cointegration analysis between economic growth and the number of granted patents based on the example of the German economy

2020 ◽  
Vol 64 (9) ◽  
pp. 87-99
Author(s):  
Janusz Myszczyszyn
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Unit Root ◽  
Statistical Data ◽  
German Economy ◽  
Correlation Coefficients ◽  
Cointegration Analysis ◽  
Cointegration Test ◽  
The Empirical Analysis

The main purpose of the article was to use the Granger cointegration test to confirm the long-term relationship between the level of economic growth in Germany and the number of granted patents, including the so-called economically valuable patents. The empirical analysis was based on available statistical data on the level of economic growth (seven time series) and the number of patents received and valuable patents in the period 1872-1913. In addition to estimates of Pearson’s correlation coefficients, tests for checking the unit root: ADF and KPSS, were used. They indicated that all the analysed time series are integrated in the first stage I(1), which enabled the use of the Engle-Granger cointegration test. The obtained research results did not confirm the long-term correlation between the level of economic growth in Germany and the number of granted patents, including the so-called economically valuable patents.

scholarly journals General methods of forecasting nonlinear nonstationary processes based on mathematical models using statistical data

2021 ◽  
pp. 79-86
Author(s):  
Oleg Belas ◽  
Andrii Belas
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Mathematical Models ◽  
Recurrent Neural Networks ◽  
Statistical Data ◽  
Economic Systems ◽  
Nonstationary Processes ◽  
General Technique ◽  
New Approach ◽  
Advantages And Disadvantages

The article considers the problem of forecasting nonlinear nonstationary processes, presented in the form of time series, which can describe the dynamics of processes in both technical and economic systems. The general technique of analysis of such data and construction of corresponding mathematical models based on autoregressive models and recurrent neural networks is described in detail. The technique is applied on practical examples while performing the comparative analysis of models of forecasting of quantity of channels of service of cellular subscribers for a given station and revealing advantages and disadvantages of each method. The need to improve the existing methodology and develop a new approach is formulated.

Statistical data characterization of in-situ nitrate level time series for the Mississippi watershed

2020 ◽  
Author(s):  
Nicholas V. Scott ◽  
Jack McCarthy ◽  
Ted Kratschmer ◽  
Miles Corcoran ◽  
Dietrick Lawrence ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Data ◽  
Nitrate Level
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