# Forecasting the Volume of Tourism Services in Uzbekistan

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{*}

## Abstract

**:**

_{2}emissions). We used a time series-univariate ARIMA model to forecast the inbound tourism in the Republic of Uzbekistan, and applied the ARDL model to assess the impact of lagged real GDP per capita on inbound tourism in both the short and long terms. The results of our research show that security and welfare significantly affect the inflow of foreign tourists in the country, along with the impact of the COVID-19 pandemic crisis, the effects of which are expected to persist beyond 2026.

## 1. Introduction

## 2. Literature Review

## 3. Working Methodology

#### 3.1. General Overview of Forecasting Methodology

#### 3.2. Overview of Uzbekistan’s Inbound Tourism Statistics

#### 3.3. Specific Methodology Application

_{2}emissions), and security level (total crimes recorded).

## 4. Materials and Methods

_{2}emissions, metric tons per capita). The data below represent the dynamics of the above-mentioned factors from 2000 to 2020.

_{1}—passenger transportation, million people

_{2}—real GDP per capita, thousand sums

_{3}—total crime records, units

_{4}—consumer price index, in percent

_{5}—CO

_{2}emissions, metric tons per capita

_{6}—life expectancy, years

_{t}−the number of inbound tourists, thousand people

_{t}−error term

_{1}, α

_{2}—corresponding coefficients, σ—mean value, and u

_{t}—uncorrelated random error term. The results of the forecasting are illustrated in Figure 3.

## 5. Results

## 6. Discussion

_{2}emissions), and the security level (total crime records). However, out of these six factors, only four factors turned out to be statistically significant. CO

_{2}emissions and consumer price index did not significantly affect inbound tourism. Other factors, for instance, life expectancy, GDP per capita, and passenger transportation volume, were strongly correlated with each other; therefore, each constituted a separate regression model. Out of three competitive models, the OLS model with the factors of life expectancy and total crime better explained the fluctuations in the inbound tourism.

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Safarov, B.; Janzakov, B. Measuring competitiveness in tourism enterprises using integral index. Geoj. Tour. Geosites
**2021**, 37, 768–774. [Google Scholar] [CrossRef] - UNWTO. Covid-19: Measures to Support the Travel and Tourism Sector; World Tourism Organization: Madrid, Spain, 2021. [Google Scholar]
- Cicort-Lucaciu, A.Ş.; Cupşa, D.; Ilieş, D.; Ilieş, A.; Baiaş, Ş.; Sas, I. Feeding of two amphibian species (Bombina variegata and Pelophylax ridibundus) from artificial habitats from Pădurea Craiului Mountains (Romania). North West. J. Zool.
**2011**, 7, 297–303. [Google Scholar] - Ilieș, D.C.; Hodor, N.; Indrie, L.; Dejeu, P.; Ilieș, A.; Albu, A.; Caciora, T.; Ilieș, M.; Barbu-Tudoran, L.; Grama, V. Investigations of the Surface of Heritage Objects and Green Bioremediation: Case Study of Artefacts from Maramureş, Romania. Appl. Sci.
**2021**, 11, 6643. [Google Scholar] [CrossRef] - Ilieş, M.; Ilieş, D.; Josan, I.; Ilieş, A.; Ilieş, G. The Gateway of Maramures Land. Geostrategical Implications in Space and Time. Annales
**2010**, 20, 469–480. [Google Scholar] - Caciora, T.; Herman, G.V.; Ilieș, A.; Baias, Ș.; Ilieș, D.C.; Josan, I.; Hodor, N. The Use of Virtual Reality to Promote Sustainable Tourism: A Case Study of Wooden Churches Historical Monuments from Romania. Remote Sens.
**2021**, 13, 1758. [Google Scholar] [CrossRef] - Ianc, R.; Alfred-Ştefan, C.-L.; Ilies, D.C.; Sas Kovacs, H. Note on the presence of Salamandra salamandra (Amphibia) in caves from Padurea Craiului Mountains, Romania. North West. J. Zool.
**2012**, 8, 202–204. [Google Scholar] [CrossRef] - Bou-Belda, E.; Indrie, L.; Ilieș, D.C.; Hodor, N.; Berdenov, Z.; Herman, G.; Caciora, T. Chitosan—A non-invasive approach for the preservation of historical textiles. Ind. Text. J.
**2020**, 71, 576–579. [Google Scholar] [CrossRef] - Dwyer, L.; Forsyth, P. Assessing the Benefits and Costs of Inbound Tourism. Ann. Tour. Res.
**1993**, 20, 751–768. [Google Scholar] [CrossRef] - Allaberganov, A.; Preko, A. Inbound international tourists’ demographics and travel motives: Views from Uzbekistan. J. Hosp. Tour. Insights
**2022**, 5, 99–115. [Google Scholar] [CrossRef] - Witt, S.F.; Witt, C.A. Forecasting Tourism Demand: A Review of Empirical Research. Int. J. Forecast.
**1995**, 11, 447–475. [Google Scholar] [CrossRef] - Goh, C.; Law, R. The methodological Progress of Tourism Demand Forecasting: A Review of related Literature. J. Travel Tour. Mark.
**2011**, 28, 296–317. [Google Scholar] [CrossRef] - Peng, B.; Song, H.; Crouch, G.I. A Meta-Analysis of International Tourism Demand Forecasting and Implications for Practice. Tour. Manag.
**2014**, 45, 181–193. [Google Scholar] [CrossRef] - Haiyan, S.; Li, G. Tourism Demand Modeling and Forecasting—A Review of Recent Research. Tour. Manag.
**2008**, 29, 203–220. [Google Scholar] - Li, S.; Chen, T.; Wang, L.; Ming, C. Effective Tourist Volume Forecasting Supported by PCA and Improved BPNN using Baidu Index. Tour. Manag.
**2018**, 68, 116–126. [Google Scholar] [CrossRef] - Hong, W.C.; Dong, Y.; Chen, L.Y.; Wei, S.Y. SVR with Hybrid Chaotic Genetic Algorithms for Tourism Demand Forecasting. Appl. Soft Comput. J.
**2011**, 11, 1881–1890. [Google Scholar] [CrossRef] - Assimakopoulos, V.; Nikolopoulos, K. The theta model: A decomposition approach to forecasting. Int. J. Forecast.
**2000**, 16, 521–530. [Google Scholar] [CrossRef] - Milenkovski, A.; Gjorgievski, M.; Nakovski, D. The Impact of the Traffic Infrastructure on the Tourist Destination. UTMS J. Econ.
**2020**, 11, 43–47. [Google Scholar] - Breda, Z.; Costa, C. Safety and Security Issues Affecting Inbound Tourism in the People’s Republic of China. In Tourism, Safety and Security: From Theory to Practice; Mansfeld, Y., Pizam, A., Eds.; Butterworth-Heinemann: Oxford, UK, 2005; ISBN 0750678984. [Google Scholar]
- Biagi, B.; Brandono, M.G.; Detotto, C. The effect of tourism on crime in Italy: A dynamic panel approach. Economics
**2012**, 6, 1–24. [Google Scholar] [CrossRef] [Green Version] - Sunlu, U. Environmental Impacts of Tourism. In Local Resources and Global Trades: Environments and Agriculture in the Mediterranean Region; Camarda, D., Grassini, L., Eds.; Options Méditerranéennes: Série A—Séminaires Méditerranéens, n. 57; CIHEAM Bari: Valenzano, Italy, 2003; pp. 263–270. [Google Scholar]
- Shmoylova, R.A.; Minashkin, V.G.; Sadovnikova, N.A.; Shuvalova, E.B. (Eds.) The Theory of Statistics; The Finances and Statistics: Moscow, Russia, 2007. [Google Scholar]
- Wendt, J.A.; Grama, V.; Ilieş, G.; Mikhaylov, A.S.; Borza, S.G.; Herman, G.V.; Bógdał-Brzezińska, A. Transport Infrastructure and Political Factors as Determinants of Tourism Development in the Cross-Border Region of Bihor and Maramureş. A Comparative Analysis. Sustainability
**2021**, 13, 5385. [Google Scholar] [CrossRef] - The Committee of Statistics of the Republic of Uzbekistan. Available online: http://stat.uz (accessed on 4 February 2022).
- Abdou, M.; Musabanganji, E.; Musahara, H. Tourism demand modelling and forecasting: A Review Literature. Afr. J. Hosp. Tour. Leis.
**2021**, 10, 1370–1393. [Google Scholar] [CrossRef] - Safarov, B. The Models of Prognosis of Regional Tourism’s Development. Perspect. Innov. Econ. Bus.
**2010**, 6, 80–83. [Google Scholar] [CrossRef] - Wooldridge, J.M. Introductory Econometrics: A Modern Approach; Cengage Learning: Boston, MA, USA, 2015. [Google Scholar]
- Wendt, J.A.; Indrie, L.; Dejeu, P.; Albu, A.; Ilieș, D.C.; Costea, M.; Caciora, T.; Ilieș, G.; Hodor, N.; Josan, I.; et al. Natural Sources in Preventive Conservation of Naturally Aged Textiles. Fibres Text. East. Eur.
**2021**, 29, 80–85. [Google Scholar] [CrossRef] - Marcu, F.; Hodor, N.; Indrie, L.; Dejeu, P.; Ilieș, M.; Albu, A.; Sandor, M.; Sicora, C.; Costea, M.; Ilieș, D.C.; et al. Microbiological, Health and Comfort Aspects of Indoor Air Quality in a Romanian Historical Wooden Church. Int. J. Environ. Res. Public Health
**2021**, 18, 9908. [Google Scholar] [CrossRef] [PubMed] - Ramazanova, N.; Toksanbaeva, S.; Berdenov, Z.; Ozgeldinova, Z.; Tursynova, T.; Zhakupov, A. Analysis of the current state of recreational resources of the nura river basin, the republic of Kazakhstan. Geoj. Tour. Geosites
**2020**, 31, 1043–1048. [Google Scholar] [CrossRef] - Siegel, A.F. Practical Business Statistics; Academic Press: Cambridge, MA, USA, 2016. [Google Scholar]
- Herman, G.V.; Ilieş, D.C.; Gaceu, O.; Ilieş, A.; Mester, C.; Ilieş, M.; Wendt, J.A.; Josan, I.; Baias, S.; Dumitru, M. Some consideration concerning the quality of groundwater in the NATURA 2000 Lunca Barcaului (Barcaului Meadow) site, Romania. J. Environ. Prot. Ecol.
**2019**, 20, 1102–1109. [Google Scholar] - Dwyer, L.; Forsyth, P.; Rao, P. The Price Competitiveness of Travel andTourisma comparison of 19 destinations. Tour. Manag.
**2000**, 21, 9–22. [Google Scholar] [CrossRef] - Gujarati, D. Basic Econometrics, 4th ed.; McGraw-Hill Companies: New York, NY, USA, 2004. [Google Scholar]
- Redhead, K. Personal Finance and Investments: A Behavioural Finance Perspective; Routledge: London, UK, 2008. [Google Scholar]
- Ilieş, A.; Grama, V. The external western Balkan border of the European Union and its borderland: Premises for building functional transborder territorial systems. Annales
**2010**, 20, 457–469. [Google Scholar] - Safarov, B. The Impact of Information and Communication Technologies in the Development of Tourism in Uzbekistan. Span. J. Rural Dev.
**2015**, 21–28. [Google Scholar] [CrossRef] - Baldigara, T.; Mamula, M. Modelling International Tourism Demand Using Seasonal ARIMA Models. Tour. Hosp. Manag.
**2015**, 21, 19–31. [Google Scholar] [CrossRef] - Elton, E.J.; Gruber, M.J.; Spitzer, J. Improved Estimates of Correlation Coefficients and Their Impact on Optimum Portfolios. Eur. Financ. Manag.
**2006**, 12, 303–318. [Google Scholar] [CrossRef] [Green Version] - Porter, M. On Competition; Harvard Business Review Press: Cambridge, MA, USA, 1998. [Google Scholar]
- Dwyer, L.; Kim, C. Destination competitiveness: A Model and Determinants. Curr. Issues Tour.
**2003**, 6, 369–414. [Google Scholar] [CrossRef] - Ritchie, B.; Crouch, G. A model of destination competitiveness/sustainability: Brazilian perspectives. Revista de Administração Pública
**2010**, 44, 1049–1066. [Google Scholar] [CrossRef] [Green Version]

Years | Real GDP Per Capita, Thousand Sums | Passenger Transportation, Mln People | Total Recorded Crimes, Units | The_Inbound_Tourism, Thousand People | Consumer Price Index (Relative to Previous Year) Percents | CO_{2} Emissions (Metric Tons per Capita) | Life Expectancy, Years |
---|---|---|---|---|---|---|---|

2000 | 77.2 | 3595.9 | 73,904 | 302.00 | 128.2 | 4.94 | 70.8 |

2001 | 113.4 | 3475.9 | 74,314 | 345.00 | 126.6 | 4.92 | 71.3 |

2002 | 161.2 | 3419.2 | 77,199 | 332.00 | 121.6 | 4.96 | 71.2 |

2003 | 230.2 | 3375.4 | 78,925 | 231.00 | 103.8 | 4.63 | 71.2 |

2004 | 293.2 | 3477.3 | 79,129 | 262.00 | 103.7 | 4.69 | 71.6 |

2005 | 349.6 | 3962.4 | 79,883 | 242.00 | 107.8 | 4.39 | 71.8 |

2006 | 449.9 | 4188.5 | 82,352 | 560.00 | 106.8 | 4.60 | 72.5 |

2007 | 592.1 | 4652.4 | 83,905 | 903.00 | 106.8 | 4.40 | 72.7 |

2008 | 782.5 | 5264.7 | 88,007 | 1069.00 | 107.8 | 4.55 | 72.9 |

2009 | 1031.2 | 5654.5 | 89,388 | 1215.00 | 107.4 | 4.12 | 72.9 |

2010 | 2038.7 | 4072.0 | 90,050 | 975.00 | 107.3 | 4.29 | 73.0 |

2011 | 2729.9 | 4507.8 | 90,617 | 1500.00 | 107.6 | 4.26 | 73.0 |

2012 | 3267.8 | 4763.0 | 90,660 | 1975.00 | 107.0 | 3.74 | 73.1 |

2013 | 3902.7 | 4909.9 | 90,152 | 1969.00 | 106.8 | 3.74 | 73.4 |

2014 | 4472.0 | 5169.9 | 89,360 | 1862.00 | 106.1 | 3.43 | 73.4 |

2015 | 5127.5 | 5380.0 | 87,946 | 1918.00 | 105.6 | 3.24 | 73.6 |

2016 | 5887.9 | 5560.4 | 87,412 | 2027.00 | 105.7 | 3.37 | 73.8 |

2017 | 6681.4 | 5679.0 | 73,692 | 2690.00 | 114.4 | 3.44 | 73.7 |

2018 | 7767.0 | 5951.5 | 49,011 | 5400.00 | 114.3 | 3.40 | 74.6 |

2019 | 9509.6 | 6025.1 | 46,089 | 6300.00 | 115.2 | 3.42 | 75.1 |

2020 | 10,737.3 | 5295.9 | 62,081 | 1500.00 | 111.1 | 3.41 | 73.4 |

Variable | Obs | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|

Real GDP per capita~t | 21 | 3152.487 | 3339.146 | 77.192 | 10,737.278 |

Passenger transport~e | 21 | 4684.798 | 899.419 | 3375.4 | 6025.106 |

Total recorded crime~s | 21 | 79,241.714 | 12,937.313 | 46,089 | 90,660 |

The inbound touris~p | 21 | 1598.905 | 1598.928 | 231 | 6300 |

CPI inpercent | 21 | 110.555 | 7.076 | 103.65 | 128.2 |

CO_{2} emissions metric~c | 21 | 4.093 | 0.603 | 3.244 | 4.962 |

Life expectancy years | 21 | 72.808 | 1.143 | 70.8 | 75.1 |

Variables | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|

Real GDP per capita~o | 1.000 | |||||

Passenger transport~e | 0.765 | 1.000 | ||||

Total recorded crime~s | −0.556 | −0.250 | 1.000 | |||

CPI inpercent | −0.039 | −0.231 | −0.467 | 1.000 | ||

CO_{2} emissions metric~c | −0.874 | −0.855 | 0.202 | 0.331 | 1.000 | |

Life expectancy years~s | 0.822 | 0.920 | −0.297 | −0.303 | −0.880 | 1.000 |

Mean dependent var | 1598.905 | SD dependent var | 1598.928 | |||

R-squared | 0.809 | Number of obs | 21 | |||

F-test | 25.945 | Prob > F | 0.000 | |||

Akaike crit. (AIC) | 341.595 | Bayesian crit. (BIC) | 345.773 | |||

The_inbound_touris~p | Coef. | St. Err. | t-value | p-value | [95% Conf. Interval] | |

Passenger transport~e | 1.124 | 0.134 | 8.36 | 0 | 0.841 | 1.408 |

Total recorded crime~s | −0.062 | 0.021 | −3.00 | 0.008 | −0.106 | −0.019 |

CPI inpercent | −10.946 | 13.526 | −0.81 | 0.43 | −39.484 | 17.591 |

Constant | 2494.176 | 3102.485 | 0.80 | 0.433 | −4051.494 | 9039.847 |

Mean dependent var | 1598.905 | SD dependent var | 1598.928 | |||

R-squared | 0.916 | Number of obs | 21 | |||

F-test | 64.319 | Prob > F | 0.000 | |||

Akaike crit. (AIC) | 324.429 | Bayesian crit. (BIC) | 328.607 | |||

The_inbound_touris~p | Coef. | St. Err. | t-value | p-value | [95% Conf. Interval] | |

Total recorded crime~s | −0.043 | 0.015 | −2.81 | 0.012 | −0.075 | −0.011 |

Life expectancy years | 1114.131 | 109.836 | 10.14 | 0 | 882.397 | 1345.865 |

CPI inpercent | 27.218 | 19.166 | 1.42 | 0.174 | −13.219 | 67.655 |

Constant | −7,9117.633 | 1,0384.738 | −7.62 | 0 | −10,1027.52 | −57,207.75 |

Mean dependent var | 1598.905 | SD dependent var | 1598.928 | |||

R-squared | 0.683 | Number of obs | 21 | |||

F-test | 3.556 | Prob > F | 0.037 | |||

Akaike crit. (AIC) | 352.295 | Bayesian crit. (BIC) | 356.473 | |||

The_inbound_touris~p | Coef. | St. Err. | t-value | p-value | [95% Conf. Interval] | |

Total recorded crime~s | −0.047 | 0.026 | −1.80 | 0.09 | −0.101 | 0.008 |

Real GDP per capita th~o | 0.272 | 0.123 | 2.22 | 0.041 | 0.013 | 0.531 |

CPI inpercent | −25.424 | 22.871 | −1.11 | 0.282 | −73.676 | 22.829 |

Constant | 7253.053 | 4486.911 | 1.62 | 0.124 | −2213.502 | 16719.609 |

Variable | Coefficient | Std. Error | t-Statistic | Prob. * |
---|---|---|---|---|

THE_INBOUND_TOURISM___THOUSAND_PEOPLE(−1) | 0.093148 | 0.365845 | 0.254610 | 0.8037 |

THE_INBOUND_TOURISM___THOUSAND_PEOPLE(−2) | −2.427990 | 0.338735 | −7.167823 | 0.0000 |

THE_INBOUND_TOURISM___THOUSAND_PEOPLE(−3) | 0.342439 | 0.733650 | 0.466761 | 0.6498 |

THE_INBOUND_TOURISM___THOUSAND_PEOPLE(−4) | −2.615914 | 0.875866 | −2.986660 | 0.0124 |

REAL_GDP_PER_CAPITA__THOUSAND_SUMS | 1.601196 | 0.326348 | 4.906405 | 0.0005 |

C | 1459.296 | 315.5607 | 4.624454 | 0.0007 |

R-squared | 0.952019 | Mean dependent var | 1903.941 | |

Adjusted R-squared | 0.930210 | S.D. dependent var | 1635.909 | |

S.E. of regression | 432.1720 | Akaike info criterion | 15.24609 | |

Sum squared resid | 2,054,499 | Schwarz criterion | 15.54016 | |

Log likelihood | −123.5918 | Hannan-Quinn criter. | 15.27532 | |

F-statistic | 43.65163 | Durbin-Watson stat | 2.114034 | |

Prob(F-statistic) | 0.000001 |

F-statistic | 0.668954 | Prob. F (4,7) | 0.6338 |

Obs*R-squared | 4.701295 | Prob. Chi-Square (4) | 0.3193 |

F-statistic | 2.016349 | Prob. F (4,8) | 0.1850 |

R-squared | 6.526458 | Prob. Chi-Square (4) | 0.1631 |

Test Statistic | Value | Signif. | I(0) | I(1) |
---|---|---|---|---|

Asymptotic: n = 1000 | ||||

F-statistic | 12.57814 | 10% | 3.02 | 3.51 |

k | 1 | 5% | 3.62 | 4.16 |

2.5% | 4.18 | 4.79 | ||

1% | 4.94 | 5.58 |

Variable | Coefficient | Std. Error | t-Statistic | Prob. |
---|---|---|---|---|

REAL_GDP_PER_CAPITA__THOUSAND_SUMS | 0.285504 | 0.009247 | 30.87488 | 0.0000 |

C | 260.2021 | 31.77712 | 8.188347 | 0.0000 |

t-Statistic | Prob.* | |||
---|---|---|---|---|

Augmented Dickey-Fuller test statistic | −4.093299 | 0.0289 | ||

Test critical values: | 1% level | −4.728363 | ||

5% level | −3.759743 | |||

10% level | −3.324976 |

Autocorrelation | Partial Correlation | AC | PAC | Q-Stat | Prob | |
---|---|---|---|---|---|---|

. | . | . | . | 1 | −0.003 | −0.003 | 0.0002 | 0.988 |

***| . | .***| . | 2 | −0.383 | −0.383 | 3.5914 | 0.166 |

. *| . | . *| . | 3 | −0.095 | −0.115 | 3.8269 | 0.281 |

. | . | . *| . | 4 | −0.020 | −0.204 | 3.8374 | 0.428 |

. | . | . *| . | 5 | 0.005 | −0.110 | 3.8383 | 0.573 |

. | . | . | . | 6 | 0.066 | −0.052 | 3.9748 | 0.680 |

. | . | . | . | 7 | 0.051 | −0.013 | 4.0628 | 0.773 |

. *| . | . *| . | 8 | −0.076 | −0.094 | 4.2755 | 0.831 |

. *| . | . *| . | 9 | −0.071 | −0.076 | 4.4745 | 0.878 |

. | . | . | . | 10 | 0.061 | −0.003 | 4.6372 | 0.914 |

. | . | . | . | 11 | 0.018 | −0.052 | 4.6538 | 0.947 |

. | . | . | . | 12 | 0.011 | 0.012 | 4.6610 | 0.968 |

. | . | . *| . | 13 | −0.043 | −0.079 | 4.7770 | 0.980 |

. | . | . | . | 14 | −0.046 | −0.053 | 4.9336 | 0.987 |

. | . | . | . | 15 | −0.003 | −0.063 | 4.9346 | 0.993 |

. | . | . *| . | 16 | −0.006 | −0.085 | 4.9387 | 0.996 |

. | . | . | . | 17 | 0.021 | −0.057 | 5.0024 | 0.998 |

. | . | . *| . | 18 | 0.010 | −0.066 | 5.0266 | 0.999 |

. | . | . | . | 19 | 0.003 | −0.039 | 5.0294 | 0.999 |

Variable | Coefficient | Std. Error | t-Statistic | Prob. |
---|---|---|---|---|

C | 165.8489 | 153.7908 | 1.078406 | 0.2959 |

AR(2) | −0.877635 | 0.194713 | −4.507337 | 0.0003 |

SIGMASQ | 806578.0 | 231229.2 | 3.488218 | 0.0028 |

R-squared | 0.502550 | Mean dependent var | 59.90000 | |

Adjusted R-squared | 0.444026 | S.D. dependent var | 1306.431 | |

S.E. of regression | 974.1228 | Akaike info criterion | 16.88551 | |

Sum squared resid | 16131560 | Schwarz criterion | 17.03487 | |

Log likelihood | −165.8551 | Hannan-Quinn criter. | 16.91466 | |

F-statistic | 8.587134 | Durbin-Watson stat | 1.445303 | |

Prob(F-statistic) | 0.002645 |

Autocorrelation | Partial Correlation | AC | PAC | Q-Stat | Prob | |
---|---|---|---|---|---|---|

. | . | . | . | 1 | 0.047 | 0.047 | 0.0503 | |

.***| . | .***| . | 2 | −0.432 | −0.435 | 4.6067 | 0.032 |

. *| . | . *| . | 3 | −0.134 | −0.105 | 5.0734 | 0.079 |

. | . | . *| . | 4 | 0.025 | −0.190 | 5.0907 | 0.165 |

. | . | . | . | 5 | 0.072 | −0.040 | 5.2428 | 0.263 |

. | . | . *| . | 6 | 0.012 | −0.093 | 5.2472 | 0.386 |

. | . | . | . | 7 | 0.014 | 0.014 | 5.2539 | 0.512 |

. | . | . *| . | 8 | −0.038 | −0.081 | 5.3081 | 0.622 |

. *| . | . | . | 9 | −0.066 | −0.061 | 5.4807 | 0.705 |

. |* . | . |* . | 10 | 0.101 | 0.077 | 5.9335 | 0.747 |

. | . | . *| . | 11 | 0.002 | −0.079 | 5.9336 | 0.821 |

. | . | . | . | 12 | −0.016 | 0.064 | 5.9477 | 0.877 |

Years | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 |
---|---|---|---|---|---|---|---|---|---|

The inbound tourism(thousand people) | 5400 | 6300 | 1500 | 1021 | 5545 | 6277 | 2618 | 2287 | 5810 |

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**MDPI and ACS Style**

Safarov, B.; Al-Smadi, H.M.; Buzrukova, M.; Janzakov, B.; Ilieş, A.; Grama, V.; Ilieș, D.C.; Csobán Vargáné, K.; Dávid, L.D.
Forecasting the Volume of Tourism Services in Uzbekistan. *Sustainability* **2022**, *14*, 7762.
https://doi.org/10.3390/su14137762

**AMA Style**

Safarov B, Al-Smadi HM, Buzrukova M, Janzakov B, Ilieş A, Grama V, Ilieș DC, Csobán Vargáné K, Dávid LD.
Forecasting the Volume of Tourism Services in Uzbekistan. *Sustainability*. 2022; 14(13):7762.
https://doi.org/10.3390/su14137762

**Chicago/Turabian Style**

Safarov, Bahodirhon, Hisham Mohammad Al-Smadi, Makhina Buzrukova, Bekzot Janzakov, Alexandru Ilieş, Vasile Grama, Dorina Camelia Ilieș, Katalin Csobán Vargáné, and Lóránt Dénes Dávid.
2022. "Forecasting the Volume of Tourism Services in Uzbekistan" *Sustainability* 14, no. 13: 7762.
https://doi.org/10.3390/su14137762