scholarly journals Problems of identifying time series intervals when predicting the dynamics of the number of infected Covid-19 by statistical methods using the example of Yugra

2021 ◽  
Vol 16 (3) ◽  
pp. 70-74
Author(s):  
Mikhail G. Korotkov ◽  
Aleksey A. Petrov ◽  
Maria V. Kurkina
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Statistical Forecasting ◽  
Coronavirus Infection

The aim of this work is to develop an approach to isolate the data interval for statistical forecasting from the time series of dynamics of new cases of coronavirus infection in the Yugra of the number of COVID-19 infected in the spring-summer of 2020.

scholarly journals Forecasting emergency department presentations

2007 ◽  
Vol 31 (1) ◽  
pp. 83 ◽  
Author(s):  
Robert Champion ◽  
Leigh D Kinsman ◽  
Geraldine A Lee ◽  
Kevin A Masman ◽  
Elizabeth A May ◽  
...  
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Time Series Analysis ◽  
Software Package ◽  
Exponential Smoothing ◽  
Statistical Forecasting ◽  
Forecasting Methods ◽  
Number Of Patients ◽  
Forecasting Performance ◽  
Optimal Forecasting

Objective: To forecast the number of patients who will present each month at the emergency department of a hospital in regional Victoria. Methods: The data on which the forecasts are based are the number of presentations in the emergency department for each month from 2000 to 2005. The statistical forecasting methods used are exponential smoothing and Box?Jenkins methods as implemented in the software package SPSS version 14.0 (SPSS Inc, Chicago, Ill, USA). Results: For the particular time series, of the available models, a simple seasonal exponential smoothing model provides optimal forecasting performance. Forecasts for the first five months in 2006 compare well with the observed attendance data. Conclusions: Time series analysis is shown to provide a useful, readily available tool for predicting emergency department demand. The approach and lessons from this experience may assist other hospitals and emergency departments to conduct their own analysis to aid planning.

scholarly journals Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods

2019 ◽  
Vol 3 (2) ◽  
pp. 274-306 ◽  
Author(s):  
Ruben Sanchez-Romero ◽  
Joseph D. Ramsey ◽  
Kun Zhang ◽  
Madelyn R. K. Glymour ◽  
Biwei Huang ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Permutation Test ◽  
Causal Structure ◽  
Multiple Model ◽  
New Methods ◽  
Test Models ◽  
Non Gaussian ◽  
True Structure ◽  
Better Than

We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback from synthetic BOLD time series. We compare an adaptation of the first correct method for recovering cyclic linear systems; Granger causal regression; a multivariate autoregressive model with a permutation test; the Group Iterative Multiple Model Estimation (GIMME) algorithm; the Ramsey et al. non-Gaussian methods; two non-Gaussian methods by Hyvärinen and Smith; a method due to Patel et al.; and the GlobalMIT algorithm. We introduce and also compare two new methods, Fast Adjacency Skewness (FASK) and Two-Step, both of which exploit non-Gaussian features of the BOLD signal. We give theoretical justifications for the latter two algorithms. Our test models include feedback structures with and without direct feedback (2-cycles), excitatory and inhibitory feedback, models using experimentally determined structural connectivities of macaques, and empirical human resting-state and task data. We find that averaged over all of our simulations, including those with 2-cycles, several of these methods have a better than 80% orientation precision (i.e., the probability of a directed edge is in the true structure given that a procedure estimates it to be so) and the two new methods also have better than 80% recall (probability of recovering an orientation in the true structure).

scholarly journals Wavelet analysis of the dynamics of waves of coronavirus infection in France, Sweden and Chine.

2021 ◽  
Vol 2021 (8) ◽  
Author(s):  
A. P. Anyutin ◽  
◽  
T. M. Khodykina ◽  
Keyword(s):  
Time Series ◽  
Wavelet Analysis ◽  
Population Size ◽  
Geographic Location ◽  
Coronavirus Infection ◽  
Wavelet Spectra

In this work, the wavelet spectra were calculated and studied for time series representing the dynamics of the new cases of coronavirus infection in France, Sweden and China. It was found that the Wavelet spectra for these countries have characteristic different-scale internal cycles, the number of which depends on the nature of the quarantine activities. It was detected that structure of the Wavelet spectra, their duration is practically independent of the geographic location, density and population size.

Detecting Determinism in Human Posture Control Data

1998 ◽  
Vol 08 (01) ◽  
pp. 179-188 ◽  
Author(s):  
L. Y. Cao ◽  
B. G. Kim ◽  
J. Kurths ◽  
S. Kim
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Nonstationary Time Series ◽  
Posture Control ◽  
Nonlinear Prediction ◽  
Control Data ◽  
Human Posture ◽  
Prediction Techniques

In this paper, determinism in human posture control data is investigated using the approach of nonlinear prediction. We first comment that one should be cautious of using some statistical methods to analyze nonstationary time series. Then we test the predictability of the human posture control data with different prediction techniques, and investigate how nonstationarity and noise affect the prediction results. Different time series are tested, including data from healthy and ill persons, and different predictabilities are found in different time series.

Study on the Application of Data Mining Based on Campus Card Platform

2013 ◽  
Vol 846-847 ◽  
pp. 977-980 ◽  
Author(s):  
Yuan Qian ◽  
Quan Shi
Keyword(s):  
College Students ◽  
Data Mining ◽  
Time Series ◽  
Statistical Methods ◽  
Business Intelligence ◽  
Sql Server ◽  
Policy Makers ◽  
University Policy ◽  
Development Tools ◽  
Intelligence Development

The thesis uses data in the database of campus card platform as the analysis object, combined with statistical methods and data mining technology to analyze the students consumption and the situation of the canteens. We use the Microsoft .NET and SQL Server 2008 business intelligence development tools to mine and analyze these data; know canteens consumption and learn about the business status and the popular shops of the canteen by using the K-means algorithm; analyze and predict students behavior and the situation of the canteen by using time series algorithm. It is convenient to manage the college students, and provide data support for university policy makers and shoppers to make plans.

Threshold models for the analysis of environmental extremes

1984 ◽  
Vol 16 (1) ◽  
pp. 20-20
Author(s):  
Richard L. Smith
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Extreme Values ◽  
Threshold Level ◽  
High Threshold ◽  
Threshold Models ◽  
Stationary Sequences ◽  
Environmental Extremes

Statistical methods for analysing the extreme values of a time series may be based on the observed exceedances of the series above a high threshold level. Todorovic (1979) has developed this approach in detail; other relevant references are North (1980) and the English Flood Studies Report (1975). One way of motivating these models is by reference to the theory of extremes in stationary sequences, due to Leadbetter and others.

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