Time series analysis in forecasting pririrodno focal infections

Abstract

Mathematical methods and models used in forecasting problems may relate to a wide variety of
 topics: from the regression analysis, time series analysis, formulation and evaluation of expert opinions, simulation,
 systems of simultaneous equations, discriminant analysis, logit and probit models, logical unit decision
 functions, variance or covariance analysis, rank correlation and contingency tables, etc.
 In the analysis of the phenomenon over a long timeperiod, for example, the incidence of long-term dynamics
 with a forecast of further development of the process, you should use the time series, which is influenced
 by the following factors:
 • Emerging trends of the series (the trend in cumulative long-term effects of many factors on the dynamics
 of the phenomenon under study - ascending or descending);
 • forming a series of cyclical fluctuations related to the seasonality of the disease;
 • random factors.
 In our study, we conducted a study to identify cyclical time series of long-term dynamics of morbidity of
 HFRS and autumn bank vole population. This study was performed using the autocorrelation coefficient.
 As a result of time-series studies of incidence of HFRS, indicators autumn bank vole population revealed
 no recurrence, and these figures are random variables, which is confirmed by three tests: nonrepeatability of time
 series, the assessment increase and decrease time-series analysis of the sum of squares. This shows that a number of indicators of the time series are random variables, contains a strong non-linear trend, to identify which need
 further analysis, for example by means of regression analysis.