By G. Sh. Tsitsiashvili
This ebook provides works on processing time sequence of observations in difficulties of meteorology, ichthyology, scientific geography, epidemiology and demography. those works were released through the authors in the final four years within the Russian journals and suggested at a variety of Russian and foreign meetings. the elemental tools of processing of time sequence within the amassed works are built algorithms for: reputation of pictures, classifications, and estimations of dispersions of fluctuations pertaining to a development. the assumption of development of the 1st algorithms is composed in learning huge outliers in time sequence. Such strategy has allowed to build very simple for realizing and fairly speedy, as to computing, algorithms of popularity of pictures and classifications and to use them within the difficulties which are characterized by means of huge volumes of empirical information.The 3rd of the desired algorithms relies on distinctive adjustments of time sequence to issues of a small pattern and bigger fluctuations. software of conventional algorithms within the thought of arrays of the empirical info calls for advanced calculations. the issues defined in provided works, are real and that is why the utilizing in them of the provided algorithms includes no longer illustrative, yet tremendous personality. the issues in query: effect of meteorological elements on severe values: trap of fish(hunchback salmon) within the Amur river, freezing within the Tatar strait, numbers contaminated via tick-borne [vernal] encephalitis and different epidemic illnesses in Primorye Territory, impression of monetary variations on a variety of age teams of the inhabitants and on dynamics of a inhabitants in towns of the Primorye Territory, effect of worldwide warming on fluctuations of floor temperature in a number of parts of the a long way East.
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Extra info for Efficient Algorithms of Time Series Processing and Their Applications
Example text
This trend is a smoothed artificial curve in which the most important for prognosis of catastrophic (critical) levels of the analyzed system characterizing the individual years or other time intervals were smoothed. Secondly, the extrapolation prognosis results depend directly on the time series length and, what is more important, are determined by the type itself of multiyear movement of the phenomenon under study which could appreciably change its character at a different time. Thirdly, the extrapolation prognosis results are of probabilistic nature and limited by confidence intervals.
V. Radchenkova it could be supposed possible to work for the extreme natural phenomena recognition. Table 4. 77 Besides, we chose the most informative (based on physical hypothesis) region, where high coefficients of recognition proved the right choice of region. Also, calculation data from the other region (Aleutian depression area) evidenced the same (Table 5). To compare the results obtained by the method of interval recognition with the most often used methods we counted the coefficient of linear correlation between ice cover and the same data on pressure in congruous regions.
Sh. V. Radchenkova Table 1. 83 Table 2. 71 1 Application of Experience Method of the Recognition by Interval… 19 The reliable information for extreme ice cover prognosis in the Tatar Strait that are used are based on the above-earth pressure over the same regions that were explored for the Н500 fields (30°-55°N, 120°-160°E). The results of calculation are present in Table 2. The data from Table 2 testify to a greater information rate (presence of great number of units) of the above-earth pressure field in July, September, October that tallies with Н500 geopotential data.
