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Forecasting US Tourists’ inflow to Slovenia by modified Holt-Winters Damped model: A case in the Tourism industry logistics and supply chains Cover

Forecasting US Tourists’ inflow to Slovenia by modified Holt-Winters Damped model: A case in the Tourism industry logistics and supply chains

Logistics, Supply Chain, Sustainability and Global Challenges
Volume 10 (2019): Issue 1 (June 2019)
By:
Open Access
|Jun 2019

References

  1. [1] www.slovenia.info. (2016). I Feel Slovenia
    Search in Google ScholarBack to article
  2. [2] N. Kovačić, D. Topolšek, and D. Dragan, “Tourism sector, Travel agencies, and Transport Suppliers: Comparison of Different Estimators in the Structural Equation Modeling,” Logistics & Sustainable Transport, vol. 6, no. 1, pp. 11-24, 2015.10.1515/jlst-2015-0007
    Search in Google ScholarBack to article
  3. [3] R. Hyndman, A. B. Koehler, J. K. Ord, and R. D. Snyder, Forecasting with exponential smoothing: the state space approach. New York: Springer Science & Business Media, 2008.10.1007/978-3-540-71918-2
    Search in Google ScholarBack to article
  4. [4] A. Gosar, “Some Characteristics of Tourism in Slovenia,” Slovene Studies Journal, vol. 12, no. 1, pp. 33-42, 1990.10.7152/ssj.v12i1.3791
    Search in Google ScholarBack to article
  5. [5] M. Konecnik and F. Go, “Tourism destination brand identity: The case of Slovenia,” Journal of Brand Management, vol. 15, no. 3, pp. 177-189, 2008.10.1057/palgrave.bm.2550114
    Search in Google ScholarBack to article
  6. [6] A. Assaf and K. L. Cvelbar, “Privatization, market competition, international attractiveness, management tenure and hotel performance: Evidence from Slovenia,” International Journal of Hospitality Management, vol. 30, no. 2, pp. 391-397, 2011.10.1016/j.ijhm.2010.03.012
    Search in Google ScholarBack to article
  7. [7] T. Mihalič, V. Žabkar, and L. K. Cvelbar, “A hotel sustainability business model: evidence from Slovenia,” Journal of Sustainable Tourism, vol. 20, no. 5, pp. 701-719, 2012.10.1080/09669582.2011.632092
    Search in Google ScholarBack to article
  8. [8] V. Žabkar, M. M. Brenčič, and T. Dmitrović, “Modelling perceived quality, visitor satisfaction and behavioural intentions at the destination level,” Tourism management, vol. 31, no. 4, pp. 537-546, 2010.10.1016/j.tourman.2009.06.005
    Search in Google ScholarBack to article
  9. [9] SSD, “Slovene: Statistični Urad Republike Slovenije,,” Statistični Urad Republike Slovenije, https://www.stat.si/statweb2016.
    Search in Google ScholarBack to article
  10. [10] J. W. Taylor, “Exponential smoothing with a damped multiplicative trend,” International journal of Forecasting, vol. 19, no. 4, pp. 715-725, 2003.10.1016/S0169-2070(03)00003-7
    Search in Google ScholarBack to article
  11. [11] R. C. Souza, M. Barros, and C. V. C. d. Miranda, “Short Term Load Forecasting Using Double Seasonal Exponential Smoothing and Interventions to Account for Holidays and Temperature Effects,” presented at the TLAIO II-2 do Taller Latino Iberoamericano de Investigación de Operaciones, Acapulco, México., 2007.
    Search in Google ScholarBack to article
  12. [12] J. Taylor, “Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing,” Journal of Operational Research Society, vol. 54, pp. 799-805, 2003.10.1057/palgrave.jors.2601589
    Search in Google ScholarBack to article
  13. [13] J. Taylor, L. Menezes, and P. McSharry, “A Comparison of Univariate Methods for Forecasting Eletricity Demand up to a Day Ahead,” International Journal of Forecasting, vol. 22, no. 1, pp. 1-16, 2006.10.1016/j.ijforecast.2005.06.006
    Search in Google ScholarBack to article
  14. [14] M. Intihar, T. Kramberger, and D. Dragan, “The relationship between the economic indicators and the accuracy of container throughput forecasting,” in IAME 2015 Conference Kuala Lumpur, Malaysia, Kuala Lumpur, 2015.
    Search in Google ScholarBack to article
  15. [15] M. Intihar, T. Kramberger, and D. Dragan, “Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model,” Promet-Traffic Transportation, vol. 29, no. 5, pp. 529-542, 2017.10.7307/ptt.v29i5.2334
    Search in Google ScholarBack to article
  16. [16] D. Dragan, T. Kramberger, A. Lisec, and M. Intihar, “The Impact of Macroeconomic Indicators on Forecasting a Total Cargo Throughput in the Adriatic Seaport,” in Symposium on Logistics (ISL 2017) Data Driven Supply Chains, 2017.
    Search in Google ScholarBack to article
  17. [17] D. N. Aratuo and X. L. Etienne, “Industry level analysis of tourism-economic growth in the United States,” Tourism Management, vol. 70, pp. 333-340, 2019.10.1016/j.tourman.2018.09.004
    Search in Google ScholarBack to article
  18. [18] WEF, The travel and tourism competitiveness report 2015. World Economic Forum, 2015.
    Search in Google ScholarBack to article
  19. [19] J. Balaguer and Cantavella-Jorda, “Tourism as a long-run economic growth factor: the Spanish case,” Applied economics, vol. 34, no. 7, pp. 877-884, 2002.10.1080/00036840110058923
    Search in Google ScholarBack to article
  20. [20] A. Lanza, P. Temple, and G. Urga, “The implications of tourism specialisation in the long run: an econometric analysis for 13 OECD economies,” Tourism Management, vol. 24, no. 3, pp. 315-321, 2003.10.1016/S0261-5177(02)00065-1
    Search in Google ScholarBack to article
  21. [21] J. G. Brida, B. Lanzilotta, S. Lionetti, and W. A. Risso, “Research note: The tourism-led growth hypothesis for Uruguay,” Journal of Policy Research in Tourism, Leisure, vol. 16, no. 3, pp. 765-771, 2010.10.5367/000000010792278356
    Search in Google ScholarBack to article
  22. [22] N. Dritsakis, “Tourism development and economic growth in seven Mediterranean countries: A panel data approach,” Tourism Economics, vol. 18, no. 4, pp. 801-816, 2012.10.5367/te.2012.0140
    Search in Google ScholarBack to article
  23. [23] M. d. P. Pablo-Romero and J. A. Molina, “Tourism and economic growth: A review of empirical literature,” Tourism Management Perspectives, vol. 8, pp. 28-41, 2013.10.1016/j.tmp.2013.05.006
    Search in Google ScholarBack to article
  24. [24] L. Gunduz and A. Hatemi-J, “Is the tourism-led growth hypothesis valid for Turkey?,” Applied Economics Letters, vol. 12, no. 8, pp. 499-504, 2005.10.1080/13504850500109865
    Search in Google ScholarBack to article
  25. [25] M. Belloumi, “The relationship between tourism receipts, real effective exchange rate and economic growth in Tunisia,” International journal of tourism research, vol. 12, no. 5, pp. 550-560, 2010.10.1002/jtr.774
    Search in Google ScholarBack to article
  26. [26] S. Proença and E. Soukiazis, “Tourism as an economic growth factor: a case study for Southern European countries,” Tourism Economics, vol. 14, no. 4, pp. 791-806, 2008.10.5367/000000008786440175
    Search in Google ScholarBack to article
  27. [27] S. Katircioğlu, “Research note: Testing the tourism-led growth hypothesis for Singapore–an empirical investigation from bounds test to cointegration and Granger causality tests,” Tourism Economics, vol. 16, no. 4, pp. 1095-1101, 2010.10.5367/te.2010.0012
    Search in Google ScholarBack to article
  28. [28] J. G. Brida, W. A. Risso, and Events, “Tourism as a determinant of long-run economic growth,” Journal of Policy Research in Tourism, Leisure, vol. 2, no. 1, pp. 14-28, 2010.10.1080/19407960903542276
    Search in Google ScholarBack to article
  29. [29] D. Dragan, A. Keshavarzsaleh, B. Jereb, and D. Topolšek, “Integration with transport suppliers and efficiency of travel agencies,” International Journal of Value Chain Management, vol. 9, no. 2, pp. 122-148, 2018.10.1504/IJVCM.2018.092388
    Search in Google ScholarBack to article
  30. [30] M.-H. Chen, “Interactions between business conditions and financial performance of tourism firms: Evidence from China and Taiwan,” Tourism Management, vol. 28, no. 1, pp. 188-203, 2007.10.1016/j.tourman.2005.11.012
    Search in Google ScholarBack to article
  31. [31] R. C. Mill and A. M. Morrison, The tourism system. Dubuque: Kendall Hunt, 2002.
    Search in Google ScholarBack to article
  32. [32] C.-H. H. Tang and S. S. Jang, “The tourism–economy causality in the United States: A sub-industry level examination,” Tourism Management, vol. 30, no. 4, pp. 553-558, 2009.10.1016/j.tourman.2008.09.009
    Search in Google ScholarBack to article
  33. [33] A. K. Adebayo and A. C. Iweka, “Optimizing the sustainability of tourism infrastructure in Nigeria through design for deconstruction framework,” American Journal of Tourism Management, vol. 3, no. 1A, pp. 13-19, 2014.
    Search in Google ScholarBack to article
  34. [34] B. Zheng and Y. Zhang, “Household expenditures for leisure tourism in the USA, 1996 and 2006,” International Journal of Tourism Research, vol. 15, no. 2, pp. 197-208, 2013.10.1002/jtr.880
    Search in Google ScholarBack to article
  35. [35] J. Khadaroo and B. Seetanah, “Transport infrastructure and tourism development,” Annals of tourism research, vol. 34, no. 4, pp. 1021-1032, 2007.10.1016/j.annals.2007.05.010
    Search in Google ScholarBack to article
  36. [36] K. M. Cheng, “Tourism demand in Hong Kong: Income, prices, and visa restrictions,” Current Issues in Tourism, vol. 15, no. 3, pp. 167-181, 2012.10.1080/13683500.2011.569011
    Search in Google ScholarBack to article
  37. [37] J. N. Goodrich, “September 11, 2001 Attack on America: Impact on Tourism Security,” Journal of Travel Tourism Marketing, vol. 11, no. 4, pp. 1-12, 2002.10.1300/J073v11n04_01
    Search in Google ScholarBack to article
  38. [38] I. Chatziantoniou, S. Degiannakis, B. Eeckels, and G. Filis, “Forecasting tourist arrivals using origin country macroeconomics,” Applied Economics, vol. 48, no. 27, pp. 2571-2585, 2016.10.1080/00036846.2015.1125434
    Search in Google ScholarBack to article
  39. [39] C. L. Morley, “A microeconomic theory of international tourism demand,” Annals of Tourism Research, vol. 19, no. 2, pp. 250-267, 1992.10.1016/0160-7383(92)90080-9
    Search in Google ScholarBack to article
  40. [40] F. Chan and C. Lim, “Spectral analysis of seasonality in tourism demand,” Mathematics Computers in Simulation, vol. 81, no. 7, pp. 1409-1418, 2011.10.1016/j.matcom.2010.06.005
    Search in Google ScholarBack to article
  41. [41] D. Gounopoulos, D. Petmezas, and D. Santamaria, “Forecasting tourist arrivals in Greece and the impact of macroeconomic shocks from the countries of tourists’ origin,” Annals of Tourism Research, vol. 39, no. 2, pp. 641-666, 2012.10.1016/j.annals.2011.09.001
    Search in Google ScholarBack to article
  42. [42] V. J. T. m. Cho, “A comparison of three different approaches to tourist arrival forecasting,” vol. 24, no. 3, pp. 323-330, 2003.10.1016/S0261-5177(02)00068-7
    Search in Google ScholarBack to article
  43. [43] F.-L. Chu, “A fractionally integrated autoregressive moving average approach to forecasting tourism demand,” Tourism Management, vol. 29, no. 1, pp. 79-88, 2008.10.1016/j.tourman.2007.04.003
    Search in Google ScholarBack to article
  44. [44] F.-L. Chu, “A piecewise linear approach to modeling and forecasting demand for Macau tourism,” Tourism Management, vol. 32, no. 6, pp. 1414-1420, 2011.10.1016/j.tourman.2011.01.018
    Search in Google ScholarBack to article
  45. [45] H. Song, G. Li, S. F. Witt, and G. Athanasopoulos, “Forecasting tourist arrivals using time-varying parameter structural time series models,” International Journal of Forecasting, vol. 27, no. 3, pp. 855-869, 2011.10.1016/j.ijforecast.2010.06.001
    Search in Google ScholarBack to article
  46. [46] H. Hassani, A. Webster, E. S. Silva, and S. Heravi, “Forecasting US tourist arrivals using optimal singular spectrum analysis,” Tourism Management, vol. 46, pp. 322-335, 2015.10.1016/j.tourman.2014.07.004
    Search in Google ScholarBack to article
  47. [47] B. Peng, H. Song, and G. Crouch, “A meta-analysis of international tourism demand forecasting and implications for practice,” Tourism Management, vol. 45, pp. 181-193, 2014.10.1016/j.tourman.2014.04.005
    Search in Google ScholarBack to article
  48. [48] S. F. Witt, H. Song, and G. Li, The advanced econometrics of tourism demand. Abingdon: Routledge, 2008.
    Search in Google ScholarBack to article
  49. [49] M. Akal, “Forecasting Turkey’s tourism revenues by ARMAX model,” Tourism Management, vol. 25, no. 5, pp. 565-580, 2004.10.1016/j.tourman.2003.08.001
    Search in Google ScholarBack to article
  50. [50] C. Lim, M. McAleer, and J. C. Min, “ARMAX modelling of international tourism demand,” Mathematics Computers in Simulation, vol. 79, no. 9, pp. 2879-2888, 2009.10.1016/j.matcom.2008.08.010
    Search in Google ScholarBack to article
  51. [51] H. Grubb and A. Mason, “Long lead-time forecasting of UK air passengers by Holt–Winters methods with damped trend,” International Journal of Forecasting, vol. 17, no. 1, pp. 71-82, 2001.10.1016/S0169-2070(00)00053-4
    Search in Google ScholarBack to article
  52. [52] G. Athanasopoulos and A. de Silva, “Multivariate exponential smoothing for forecasting tourist arrivals,” Journal of Travel Research, vol. 51, no. 5, pp. 640-652, 2012.10.1177/0047287511434115
    Search in Google ScholarBack to article
  53. [53] P. Dimitrov, M. Kalinova, G. Gantchev, and C. Nikolov, “Exponential forecasting of the monthly volume of the tourism receipts in Bulgaria,” Tourism Management Studies, vol. 11, no. 1, pp. 104-110, 2015.
    Search in Google ScholarBack to article
  54. [54] D. O. Gomezelj and T. Mihalič, “Destination competitiveness—Applying different models, the case of Slovenia,” Tourism management, vol. 29, no. 2, pp. 294-307, 2008.10.1016/j.tourman.2007.03.009
    Search in Google ScholarBack to article
  55. [55] T.-. SURS, Annual Report on Tourism 2015 - Slovenia (National annual reports on tourism). Ljubljana: Statistical Office of the Republic of Slovenia, 2015.
    Search in Google ScholarBack to article
  56. [56] T. W. T. T. C. (WTTC), Travel & Tourism: Economic Impact 2015-Slovenia London: The World Travel & Tourism Council (WTTC), 2015.
    Search in Google ScholarBack to article
  57. [57] R. J. Hyndman, A. B. Koehler, R. D. Snyder, and S. Grose, “A state space framework for automatic forecasting using exponential smoothing methods,” International Journal of forecasting, vol. 18, no. 3, pp. 439-454, 2002.10.1016/S0169-2070(01)00110-8
    Search in Google ScholarBack to article
  58. [58] R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” International journal of forecasting, vol. 22, no. 4, pp. 679-688, 2006.10.1016/j.ijforecast.2006.03.001
    Search in Google ScholarBack to article
  59. [59] L. F. Tratar, B. Mojškerc, and A. Toman, “Demand forecasting with four-parameter exponential smoothing,” International Journal of Production Economics, vol. 181, pp. 162-173, 2016.10.1016/j.ijpe.2016.08.004
    Search in Google ScholarBack to article
  60. [60] E. S. Gardner Jr and E. McKenzie, “Forecasting trends in time series,” Management Science, vol. 31, no. 10, pp. 1237-1246, 1985.10.1287/mnsc.31.10.1237
    Search in Google ScholarBack to article
  61. [61] E. S. Gardner Jr and E. McKenzie, “Note—Seasonal exponential smoothing with damped trends,” Management Science, vol. 35, no. 3, pp. 372-376, 1989.10.1287/mnsc.35.3.372
    Search in Google ScholarBack to article
  62. [62] H. B. Mitchell, Data fusion: concepts and ideas. New York: Springer Science & Business Media, 2012.10.1007/978-3-642-27222-6
    Search in Google ScholarBack to article
  63. [63] J. Sueur, Sound Analysis and Synthesis with R. Basel: Springer 2018.10.1007/978-3-319-77647-7
    Search in Google ScholarBack to article
  64. [64] John E. Hanke and D. Wichern, Business Forecasting: Pearson New International Edition (9e). Melbourne: Pearson Australia Group Pty Ltd, 2013.
    Search in Google ScholarBack to article
  65. [65] Bruce L. Bowerman, Richard O’Connell, and A. Koehler, Forecasting, Time Series, and Regression London: Cengage Learning, 2005.
    Search in Google ScholarBack to article
  66. [66] D. Dragan, T. Kramberger, and M. Intihar, “A comparison of Methods for Forecasting the Container Throughput in North Adriatic Ports,” in Conference IAME, Norfolk, 2014.
    Search in Google ScholarBack to article
  67. [67] J. A. D. Jennifer L. Castle and F. H. David, Some forecasting principles from the M4 competition. Magdelen College and Nuffield College, 2019.
    Search in Google ScholarBack to article
  68. [68] S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The M4 Competition: Results, findings, conclusion and way forward,” International Journal of Forecasting ·, vol. 34, no. 4, pp. 802-808, 2018.10.1016/j.ijforecast.2018.06.001
    Search in Google ScholarBack to article
  69. [69] G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control. New York: John Wiley & Sons, 2015.
    Search in Google ScholarBack to article
  70. [70] L. Ljung, System identification: theory for the user. Upper Saddle River, New Jersey: Prentice-Hall, 1987, pp. 519-519.
    Search in Google ScholarBack to article
  71. [71] L. Ljung, System Identification Toolbox for use with MATLAB. Nattick: Mathworks, 2011.
    Search in Google ScholarBack to article
  72. [72] S. Gričar and Š. Bojnec, Aplikacija metodologije časovnih serij na primeru turističnih cen. Koper: Založba Univerze na Primorskem, 2016.
    Search in Google ScholarBack to article
  73. [73] R. Powell, “Risk scenarios,” in “Asia Regional Strategic Forecast: Preparing For The Paradigm Shift. Globalisation and the Asian business environment: The Economist Corporate Network Risk scenarios,” The Economist Corporate Network., SouthEast Asia2017.
    Search in Google ScholarBack to article
  74. [74] N. Rahman, M. Muridan, and A. Najib, “A Maritime Forecasting Method for Analysing the Total Cargo Handling at Johor Port Berhad from 2013 to 2020,” International Journal of Business Management Economic Research, vol. 6, no. 3, pp. 187-93, 2015.
    Search in Google ScholarBack to article
  75. [75] L. Hsieh, “Roots of the next financial crisis.,” ed. Reuters: Reuters, 2017.
    Search in Google ScholarBack to article
  76. [76] W. Buiter, E. Rahbari, M. Saunders, S. Luna, J. Hale, and G. Menuet, Is China leading the world into recession (Global Economic Views, Citi Research). New York,: Citigroup Global Markets Inc., 2015.
    Search in Google ScholarBack to article
  77. [77] G. Xie, S. Wang, Y. Zhao, and K. K. Lai, “Hybrid approaches based on LSSVR model for container throughput forecasting: a comparative study,” Applied Soft Computing, vol. 13, no. 5, pp. 2232-2241, 2013.10.1016/j.asoc.2013.02.002
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/jlst-2019-0002 | Journal eISSN: 2784-7497
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Language: English
Page range: 11 - 30
Submitted on: Apr 12, 2019
Published on: Jun 28, 2019
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Keywords:
Tourism industry logistics and supply chains,
Tourism forecasting,
US tourists and Slovenian Tourism,
Damped Holt-Winters additive model,
Time series analysis and prediction
Related subjects:
Business and economics,
Business management,
Business management, other,
Industries,
Transportation, logistics, air traffic, shipping,
Environmental management,
Business informatics,
Process management

© 2019 Dejan Dragan, Abolfazl Keshavarzsaleh, Tomaž Kramberger, Borut Jereb, Maja Rosi, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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