Rossi Tourism Industry Development

There is a large number of cultural and natural sites in Russia. According to Rosstat, 2,368 museums in 477 historical cities, 590 theatres, 67 circus, 24 zoo, nearly 99,000 historical and cultural monuments, 140 national parks and reserves. There are currently 103 preservation museums and 41 statehood museums in Russia. " The UNESCO World Heritage List includes 23 cultural and nature sites from different regions of Russia " [1, c. 58]. However, despite this enormous tourist potential, Russia is a donor country, an active supplier of tourists to foreign countries. Entry tourism is very weak. According to UNWTO data, Russia accounts for only 2 per cent of the world ' s tourist market, which is, of course, a very low indicator for a country with such tourist and recreational potential.

The aim of the study is to predict possible options for the development of the tourism and hospitality industry in the Russian Federation. Different forecasting methods are currently used in practice: expert estimation techniques, statistical forecasting techniques, systemic and structural, demand elasticity prognosis method, economic and mathematical modelling, etc. The selection of the forecasting method depends on the purpose and duration of the research period (short-term, medium-term, long-term), on the baseline data required by accuracy, on the nature of the processing of the baseline information. In our case, statistics have been collected on the number of international tourists visiting Russia, 2006-2011, revenues received by Russia from the tourism industry, 2006-2011, the number of Russians leaving abroad in 2006-2011. ♪ These dynamic series allow regression analysis for the short and medium-term forecasting horizon. One of the most accessible software products for this type of task is MS Excel, where it is possible to add selected regressions (lines of trend) to a diagram based on the data table, and to extend the trend line in the diagram beyond real data to predict future values. The trend line illustrates trends in data and helps to analyse projections. The type of line (linear, logarithmic, polynomial, gradual, exponential, linear filtering) should be chosen according to the type of data available. More than 10 samples of the observed parameter are desirable for a more accurate forecast. Look at the possibility of trend forecasting for the collected statistics♪