Levan Soroznishvili, MA in Organizational Management N.E. Jukovsky National Air – Space University “Hi”
Forecasting –is the method that enables to predict on the basis of experience gained in the past, grounded assumes, hypotheses and estimations.
The following varieties of forecasts are especially frequently used in organization:
1. Economic forecast, used to determine general condition of economy as a whole and available sale volume of the product of the concrete company;
2. Technology Development Forecast makes it possible to define the creation of new technologies, sort and time of their economic effect;
3. Forecast of Competitiveness Development enables us to estimate strategy and tactics of our competitor;
4. Research and inquiry based forecast enables us to estimate results of expected condition, on the basis of gained knowledge in different fields;
5. Social Forecasting – estimation of upcoming changes in the society and quality of standard of living.
Forecasting requires sorting methods (filling in application forms, questioners, experts` estimation); quantity methods (Correlation analysis, Regression analysis, Statistical Analysis of Factors, Economic and Mathematical modeling etc); Methods of optimal decision-making (Game Theory, theory of mass service).
The situation created in aviation of any country is depended on ongoing events in the country. Georgia of course is not an exception, and every problem arising in economy and politics of Georgia immediately develops into decrease of travelers and cargo float, as well as carriers on aviation market.
The following are basic causes of air traffic volume reduction: poorly developed tourism and weak economic relationships with neighboring countries; important decrease in Georgia’s GDP and population incomes, unstable political environment in the country, especially in the region of Abkhazia; complete disorder of domestic flight service.
Due to current conditions in our country it is difficult to provide a long term forecast.
For instance, after the year of 1992, according to all indicators 2001 was considered to be the hardest year. Therefore, progress achieved in 2002 may be assumed as positive as well as negative. Number of carried passengers is just 270 000, much less than it had been predicted, but more in compression with the year of 2001. .
Considering aircraft movement in details, we’ll see that number of arrived passengers to Georgia is less than departed. Besides, the number of brought air cargo to the country exceeds the number of sent one, that is a negative indicator.
Forecasting Georgia’ s air craft development using approved general methods turned out to be quite difficult, due to lack of information on transportation volume and unstable economic and political conditions of the country for the past decade.
All these factors, as well as drastic change and instability of air traffic, gives no possibility to use observation and econometric observation estimation methods.
Due to above mentioned challenges, forecast of air traffic growth is provided on the basis of market estimations, on outcomes of experts’ analysis concerning local factors in regions, on presumable stability of the region, steady economic development and increase of tourism, on development perspectives of local air companies, on research works provided by airports, “Sakaeronavigatsia”. ICAO prognosis and other organizations.
According to the experts` recommendations, while preparing presumable air traffic development forecast and planning airport development and modernization, it` s better to be mistaken in gain then in deficiency. Besides, in case of stability, economic growth and development of tourism, in major airports of Georgia air traffic volume will presumably be forecasted by means of ICAO (International Civil Aviation Organization).
Global forecast of air traffic, prepared by ICAO and IATA (International Air Transport Association), “BOING”, and “Airbus Industries”, suggest to increase forecast of volume in 6- 10 % for the next decade.
Among various methods of forecasting, we can distinguish the one of several economic indicators by linear Regressive model, the task arises while planning perspectives. Such sort of forecasting is provided by means of statistical and mathematic modeling. It is obvious, that in the first case the outcome depends on experts` qualification and experience, while mathematic modeling, using selected method and algorithm for estimation enables us to make more precise forecast 
Let`s create Linear Regressive Model for forecasting, the main idea of which lies in the following: it presents the studied volume as a sum of separate adding, each of adding up point is multiply of unknown coefficient and given acting factor. Definition of unknown coefficient is provided on the basis of the next statistical data. 
For instance, if in the case of, t1,t2,…,tn it`s known that meanings of studied economic indicators are y1,y2,..,yn let us provide forecast by means of the following linear regressive model:
Where Are moments of given time C*0 and C*1 are unknown coefficients, defined by means of given statistical data .
In order to compute unknown C*0 and C*1 let us use least square method. Let`s select C*0 and C*1 coefficients with the following condition: between Yk and Y*k variables, sum of variable squares to be minimal, i.e.
to provide minimizing of following sum:
So, in order to compute searching C*0 and C*1 we got (3) and (4) formulas.
Let`s discuss the following example. We have statistical data of air passengers carried by one of flights for the period of four month: 
Let us provide a forecast of air passengers for upcoming months. For computing C*0 and C*1 constancy let us create the following chart:
Let’s put data (3) and (4) of the table in formulas and compute C*0 and C*1 coefficients.
And by means of them provide forecast of any next month, namely of November. According to tk=5 formula (1) we will get:
Instead of suggested formula (1)
So, according to (5) formula, we will get
Inaccuracy (1) and (5) between formulas is 0, 02 %.
Conclusion: the present article deals with the perspective methods of air traffic, regressive model of passenger forecast and inaccuracy reduction of the model. The suggested methods enable an air company to define passenger float through forecast and to reduce undetermined expanses.