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FORECASTING OF THE NUMBER OF THE TOURISM ARRIVALS IN SOUTH-WEST BULGARIA

Project: LOcal products Festivals and Tourism development in cross-border cooperation Greece-Bulgaria, funded under the European Territorial Cooperation Programme „Greece – Bulgaria 2007-2013.”, Subsidy Contract No. B2.12.03/03.06.2013

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FORECASTING OF THE NUMBER OF THE TOURISM ARRIVALS IN SOUTH-WEST BULGARIA

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  1. Project: LOcal products Festivals and Tourism development in cross-border cooperation Greece-Bulgaria, funded under the European Territorial Cooperation Programme „Greece – Bulgaria 2007-2013.”, Subsidy Contract No. B2.12.03/03.06.2013 The programme is funded by the European Union (ERDF) and National funds of Greece and Bulgaria FORECASTING OF THE NUMBER OF THE TOURISM ARRIVALS IN SOUTH-WEST BULGARIA Associate Prof. Dr. Preslav Dimitrov Head of the LOFT Project Implementation team at The South-West University “NeofitRilski”, Blagoevgrad, Bulgaria preslav.dimitrov@mail.bg, preslav.dimitrov@swu.bg

  2. 1. INTRODUCTION (1 of 6) • The cross border regions of South-West Bulgaria and Northern Greece have incurred an increasing number of the tourism arrivals since the accession of both Bulgaria and Romania to the European Union in 2007. The European territorial cooperation programme “Greece – Bulgaria 2007 – 2013” has also contributed to the joint cross border development of tourism in these regions by a certain number of projects.

  3. 1. INTRODUCTION (2 of 6) • One of the last projects funded by theEuropean territorial cooperation programme “Greece – Bulgaria 2007 – 2013”Programmeis the project named “LOcal products Festivals and Tourism development in cross-border cooperation Greece-Bulgaria” (LOFT). The project aims to serve a dual purpose: • (i) to promote and reinforce local cultural events, which are directly linked to local production in the cross-border region of Greece and Bulgaria; • (ii) to further promote typical local traditional products widely recognized as an essential part of the local culture and of the branding of the eligible cross border regions. • The completion of these project goals and especially the envisaged study of the impact of the local fairs and festivals on the number of the tourism arrivals need to be backed up by some preliminary analysis and forecasts. This is especially valid for the network of organizers of local fairs and festivals that should serve a booster for the joint cross border development of tourism in both South-West Bulgaria and Northern Greece.

  4. 1. INTRODUCTION (3 of 6) • The South-West Bulgaria comes into the NUTS II region BG41, known also as Yugozapaden region, and comprises mostly the NUTS III region of Blagoevgrad according to the classification used by the European Union (Graphic 1). • The territory of South-West Bulgaria also falls into the geographical scope of the “Rila and Pirin” tourism region (or the “Rila and Pirin” tourism “locus” according to the Bulgarian Tourism Act), which was visited in 2009 by 7.0% and in 2010 by 7.7% of all the Bulgarians who had their summer vacation within the country. In 2010 the tourism region, to which the territory of South-West Bulgaria belongs, was visited by 18.1% of all the foreign tourists who visited Bulgaria in the summer tourism season (Graphic 2).

  5. Graphic 1: The NUTS III regions of Bulgaria 1. INTRODUCTION (4 of 6)

  6. 1. INTRODUCTION (5 of 6) Graphic 2: The tourism regions of Bulgaria

  7. 1. INTRODUCTION (6 of 6) • Taking into account the data about the share of the South-West Bulgaria in the country’s national tourism receiving market, as well as the general information about the performance of the Bulgarian tourism sector allows a search for a suitable forecasting model for the number of the tourism arrivals in this close to Greece border region. A possible solution in this regards can be found in the face of the so-called “univariate” methods (DeLurgio, 1998, p.21) and namely and most particularly in the group of the exponential smoothing methods. • This group of methods relies on the assumption that if a considerably long time series of a certain indicator can be composed, this very same considerably long time series will have reflected all the possible external influences induced by all the possible external factors and thus time series will have incurred an internal logic of development and an internal information signal could be extrapolated further in future. The building up of forecast model, especially with the use of the exponential smoothing methods, however, needs a more sophisticated and multistage approach with a certain number of clearly set objectives.

  8. 2. OBJECTIVES • The task of creating an exponential smoothing forecast model for the number of tourism arrivals in south-west Bulgaria, meets with solving of several major problems: (i) Finding of a suitable general indicator, on the basis of which to build the forecasts; (ii) Determining the time series pattern, or the so-called “forecast profile” (Gardner, 1987, pp.174-175) (Hyndman, Koehler, Ord & Snyder, 2008, pp.11-23) andthe quality of the data in the pattern, on the basis of which to select the suitable forecasting exponential smoothing model. (iii) Selecting and using of suitable forecasting techniques; (iv) Calculating the forecast values of the above-mentioned general indicator (up to the year 2024); (v) Comparing the results of the forecast techniques (the forecast models) on the basis of the errors in the forecasts. (vi) Estimating the size of South-West Bulgaria in certain terms, so that the forecast(s) of the above-mentioned general indicator could be particularized especially for the needs of this region.

  9. 3. METHODOLOGY AND MAIN RESULTS (1 of 18) • With regards to the first problem, set in the previous point of the present paper, i.e. the difficulties in finding of a general suitable indicator, on the basis of which to make the forecast, it can be pointed out that they come mainly from the lack of reliability and the sustainability of the existing data for the separate types of indicators for tourism demand, especially in terms of time. • A greater part of the existing indicators are inconsistent in time and they lack enough data which would allow the building of sufficiently long time series. • The sole indicator which allows a comparatively long and sustainable time series to be built is the indicator “number of foreign visitors with recreation and holiday aims”, which continues to be recorded by both the former State Tourism Agency (now part of Bulgaria’s Ministry of Economy, Energy and Tourism) and the Bulgarian National Statistical Institute as a part of the indicator “number of the foreign citizens visiting Bulgaria with tourism aims”. • Taking into account the annual data available for the indicator “number of foreign visitors with recreation and holiday aims”, one can build a time series of 49 time periods (Graphic 3) – from 1964 to the last year of recorded value 2012.

  10. 3. METHODOLOGY AND MAIN RESULTS (2 of 18) Graphic 3: Number of foreign visitors in Bulgaria with recreation and holiday aims for the time period 1964 – 2012 (in thousands)

  11. 3. METHODOLOGY AND MAIN RESULTS (3of 18) • The second problem of determining the times series pattern, or the so-called times series’ “forecast profile” is usually solved by comparing the times series in regard with a pre-set classification of exponential smoothing methods or the derived form them forecast profiles in terms of development curves. As Hyndman, Koehler, Ord and Snyder point out (Hyndman et al., 2008, pp.11-12), this classification of smoothing methods originated with Pegles’ taxonomy (Pegles, 1969, pp.311-315). This was later extended by Gardner (Gardner, 1985, pp.1-28) and modified by Hyndman et al. (2002, 2008) and extended by Taylor (Taylor, 2003, pp.715-725) giving a classification set of fifteen models (Table 1). Table 1: Classification of forecasting methods Source: Hyndman et al. (2008), p.12

  12. 3. METHODOLOGY AND MAIN RESULTS (4of 18) • A simple visual analysis of the times series of the number of foreign visitors in Bulgaria with recreation and holiday aims for the time period 1964 – 2012 with Hyndman et al and Taylor’s classification shows out that these particular time series can be associated to one of the following group of forecasting patterns (forecasting profiles according to the Garnder’s classification): (i) to the “linear trend, non-seasonal” profile (“A,N” variation of Taylor’s patterns); or (ii) to the “linear trend, multiplicative seasonality” profile (A,M pattern); and (iii) to the “liner trend, additive seasonality” profile (A,A pattern). The last two groups of profiles reflecting seasonality can be accepted on the condition that the seasonality profiles are being used to describe the occurring cyclical fluctuation in the time series. • A more detailed visual review of the regarded times series on the basis of the fluctuations maxima and minima shows out that there are several types of cycles inherent in the time series, namely: (i) the Kitchin cycles of 3 to 5 years (Kitchin, 1923); (ii) the Juglar cycles of 7 to 11 years (Juglar, 1862); (iii) the Labrus Cycles of 10 to 12 years (Kuzyk & Yakovets, 2006) (iv) the Kuznets cycles of 15 to 25 years (Kuznets, 1930). This finding can be further used in the process of selecting the proper forecasting technique.

  13. 3. METHODOLOGY AND MAIN RESULTS (5 of 18) • The finding that the time series of the number of foreign visitors in Bulgaria with recreation and holiday aims for the time period 1964 – 2012 have clearly expressed different types of cycles it correspond either to the to the “linear trend, multiplicative seasonality” profile (A,M pattern), or to the “liner trend, additive seasonality” profile (A,A pattern) provides a solution to the third problem, the one of selecting and using of a suitable forecasting exponential smoothing method. As both Gardner and Hyndman et al. point out these profiles corresponds to the method of the triple exponential smoothing in the presence of a linear trend and multiplicative or additive seasonality, known as the Holt-Winters method.

  14. 3. METHODOLOGY AND MAIN RESULTS (6 of 18) • The mathematical notation of the Holt-Winters method for multiplicative seasonality is as follows:

  15. 3. METHODOLOGY AND MAIN RESULTS (7 of 18) • Respectfully, the mathematical notation the Holt-Winters method for additive seasonality is as follows:

  16. 3. METHODOLOGY AND MAIN RESULTS (8 of 18) • The initialization of the values of the level “B”, the trend “Т” and the seasonal factor “S” is achieved though the following set of equations:

  17. 3. METHODOLOGY AND MAIN RESULTS (9 of 18) • In this situation, it would be useful if the selected method for forecasting through exponential smoothing – the method of the tripe exponential smoothing (the Holt-Winter’s method) is tested in its both versions of multiplicative and additive seasonality, as the rate of increase in the fluctuation of the different cycles does not provide obvious proofs in support of either of the both versions. These tests can further be expanded to cover all the types of economic cycles found in the time series of the number of foreign visitors in Bulgaria with recreation and holiday aims for the time period 1964 – 2012. They will also provide grounds for comparing of the results in regards to the forecast error and for its minimizing in terms of the criterion “mean absolute percentage of error (MAPE)”. • The set of smoothing constants that was chosen to be applied in the above described tests are α=0.2, β=0.4 and γ=0.6. The values of the coefficients were purposefully chosen as preliminary fixed being ones of the most popular and thus allowing to outline more clearly the effect of the inherent in the time series cycles on the achieved forecasts. • For the purpose of visualization of the results from the different forecast methods for past and future periods, as well as the extent of achieved error (in comparison of the forecast values with the actually observed ones for the past periods of time), these results are presented in table and graphic form in Table 2, Table 3 and Graphics 4 and 5.

  18. 3. METHODOLOGY AND MAIN RESULTS (10of 18) Table 2: An example of forecast calculation though the use of Holt-Winters method, A,M pattern, L=4 (4 year Kitchin’s cycle)

  19. Graphic 4: Plotting of the forecast calculations achieved though the use of Holt-Winters method, A,A pattern, L=20 (cycles with twenty time periods) 3. METHODOLOGY AND MAIN RESULTS (11of 18)

  20. 3. METHODOLOGY AND MAIN RESULTS (12of 18) Table 3: Summary of the achieved forecast calculations though the use of Holt-Winters method

  21. Graphic 5: Plotting of the forecast calculations achieved though the use of Holt-Winters method, A,M and A,M patterns, L=4,8,12,20,24 3. METHODOLOGY AND MAIN RESULTS (13of 18)

  22. 3. METHODOLOGY AND MAIN RESULTS (14 of 18) • Based on the results in Table 3 and Graphic 8, one can outline three major types of forecasts for the number of the foreign visitors with recreation and holiday aims for 2024, as follows: • A pessimistic forecast (the forecast with the lowest value) – calculated by the Holt-Winters method, A,A pattern, L=8 (cycle with8 periods) with α=0.2, β=0.4 and γ=0.6: 3 459 000 foreign visitors; • The forecast with the lowest mean absolute percentage of error (MAPE) – calculated by the Holt-Winters method, A,A pattern, L=20 (cycle with 20 periods) with α=0.2, β=0.4 and γ=0.6: 6 107 000 foreign visitors; • The most optimistic forecast (the forecast with the highest value) – calculated by the Holt-Winters method, A,M pattern, L=24 (cycle with 24 periods) with α=0.2, β=0.4 and γ=0.6: 8 753 000 foreign visitors.

  23. 3. METHODOLOGY AND MAIN RESULTS (15 of 18) • All these forecasts, as well as the forecasts presented in Table 2 and Graphic 5, have one major disadvantage – they are produced for the general indicator “number of foreign visitors in Bulgaria with recreation and holiday aims”, which means that they refer to the whole of Bulgarian tourism industry and not to the sub-sector of ecotourism and the part of which belong to South-West Bulgaria. In order to overcome this disadvantage and solve problem v “estimating the size of South-West Bulgaria in certain terms, so that the forecast(s) of the above-mentioned general indicator could be particularized especially for the needs of this region”, a certain modification is needed.

  24. 3. METHODOLOGY AND MAIN RESULTS (16 of 18) • One way of doing so is by the use of a weight coefficient which shall indicate the share of the foreign visitors with intention to practice tourism in South-West Bulgaria. Thus, equations (4) and (8) can be modified into equations (12) and 13, as follows:

  25. 3. METHODOLOGY AND MAIN RESULTS (17 of 18) • Neither the Bulgarian National Statistical Institute (NSI), nor the Bulgarian Ministry of Economy, Energy and Tourism, nor any other Bulgarian government institution keeps a regular statistical record of the foreign visitors with tourism aims in the separate regions of Bulgaria. However, as it was already pointed in Graphic 2, in 2010 the tourism region, to which the territory of South-West Bulgaria belongs, was visited by 18.1% of all the foreign tourists who visited Bulgaria in the summer tourism season. This percentage figure can be used as a substitute for value of the KRT (the coefficient of the share of foreign visitors in the region of South-West Bulgaria).

  26. 3. METHODOLOGY AND MAIN RESULTS (18 of 18) • Having set the value for KRT and using equations (12) and (13), as well as the data in Graphic 3 and 5 and Table 3, the forecasts of the number of the foreign visitors in South-West Bulgaria up to 2024 can be easily made. An even simpler way to do some of the necessary calculations is to multiply the already presented pessimistic, most optimistic and lowest MAPE level forecasts for the general indicator “number of foreign visitors with recreation and holiday aims” by the decimal value of KRT, i.e. 0.1810, as follows:

  27. 4. CONCLUSIONS • The presented pessimistic, most optimistic and lowest MAPE level forecasts for the number of foreign visitors’ arrivals in South-West Bulgaria suggest that by 2024 it will vary roughly between 626079 and 1584293. This wide difference between the most pessimistic and the most optimistic forecast can be overcome with strong marketing and promotional efforts including also a joint cross border tourism marketing with the regions of Northern Greece. The latter cane be used as both image boosters and emitting tourism markets. • The presented in the paper forecasting technology, though having many shortcomings, could be applied also for municipalities and other regional units in other countries, which have unsteady and insufficient statistical tourism records. The main precondition for using this forecasting technology is to have a sustainable time series of a general tourism indicator such as “number of foreign visitors” and at least some clue about both the size of the tourism sector in the regarded tourism region.

  28. THANK YOU FOR YOUR ATTENTION!!! Associate Prof. Dr. Preslav Dimitrov South-West University “NeofitRilski”, Blagoevgrad, Bulgaria preslav.dimitrov@mail.bg, preslav.dimitrov@swu.bg

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