FORECASTING OF THE NUMBER OF THE TOURISM ARRIVALS IN SOUTH-WEST BULGARIA Associate Prof. Dr. Preslav Dimitrov Head of the LOFT Project Implementation team

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 “Neofit Rilski”, Blagoevgrad, Bulgaria

[email protected], [email protected]

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



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.




One of the last projects funded by theEuropean territorial cooperation programme “Greece – Bulgaria 2007 – 2013” Programme is 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.




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).



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




Graphic 2: The tourism regions of Bulgaria




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.



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) and the 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.



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.




Graphic 3: Number of foreign visitors in Bulgaria with recreation and holiday aims for the time period 1964 – 2012 (in thousands)

0

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294365

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8841007

125613061306129913581488

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194219442077

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26232721

21922336

19732085

2354

2756

2993

3532

40104090

43654219

4766

3810

4228

44844623

Total number of foreign visitors with recreation and holiday aims (in thousands)

Total number of foreign visitors with recreation and holiday aims (in thousands)




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).

Trend component Seasonal component N

(None) A

(Additive) M

(Multiplicative) N (None) N,N N,A N,M A (Additive) A,N A,A A,M Ad (Additive damped) Ad,N Ad,A Ad,M

M (Multiplicative) M,N M,A M,M Md (Multiplicative damped) Md,N Md,A Md,M

Table 1: Classification of forecasting methods

Source: Hyndman et al. (2008), p.12




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.




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.




The mathematical notation of the Holt-Winters method for multiplicative seasonality is as follows:

The smoothing of the level (the base) – “B”:

(1) ))(1( 11

ttLt

tt TB

S

YB 10

The smoothing of the trend – “Т”:

(2) 11 )1()( tttt TBBT 10

The smoothing of the seasonal factor – “S”:

(3) Ltt

tt S

B

YS )1( 10

The achieving of the final forecast “Ft+m” for “t+m” periods ahead in the future:

(4) Lmtttmt SmTBF )( 11 ,




Respectfully, the mathematical notation the Holt-Winters method for additive seasonality is as follows:

The smoothing of the level (the base) – “B”:

(5) ))(1()( 11 ttLttt TBSYB 10

The smoothing of the trend – “Т”:

(6) 11 )1()( tttt TBBT 10

The smoothing of the seasonal factor – “S”:

(7) Ltttt SBYS )1()( 10

The achieving of the final forecast “Ft+m” for “t+m” periods ahead in the future:

(8) Lmtttmt SmTBF 11 ,

Where: „α”, „β” and “γ” are the smoothing constants for the level, the trend and the seasonality respectfully which could take values between 0 and 1.




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: For the level (the base) – “B0”:

(9) )...(1

210 LYYYL

B

For the trend – “Т0”:

(10) )...(1 2211

0 L

YY

L

YY

L

YY

LT LLLLL

For the seasonal factor – “S0”:

(11)

N

j j

ijL

A

Y

NS

1

)1(0

1 Li ,...,2,1 ,

Where:

L

YA

L

i ijL

j

1 )1(

Nj ,...,2,1 ,

And Aj is the average value of Y in the jth cycle of the regarded time series.




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.




Table 2: An example of forecast calculation though the use of Holt-Winters method, A,M pattern, L=4 (4 year Kitchin’s cycle)

T0 166,6875

Year number of foreign visitors

Total number of foreign visitors with recreation

and holiday aims (in thousands)

Bt Tt St Ft+m, A,M, L=4

I II III IV V VI1964 294 552,000000 166,687500 0,2239145661965 365 552,000000 166,687500 0,2545985081966 665 552,000000 166,687500 0,2654074421967 884 552,000000 166,687500 0,2771995961968 1007 1207,700196 362,292578 0,589855552 1611969 1256 1662,977595 399,486507 0,555002454 4001970 1306 1994,940106 372,476908 0,498956724 5471971 1306 2240,251873 321,610852 0,460661936 6561972 1299 1975,359633 87,009615 0,630503286 15111973 1358 2000,047220 62,080804 0,629391363 11451974 1488 2146,817645 95,956652 0,615454062 10291975 1598 2334,473053 132,636155 0,594978447 10331976 1527 2245,844473 44,130261 0,660154764 15561977 1739 2313,968721 53,727856 0,702670160 14411978 2097 2489,640790 102,505541 0,751555735 14571979 2192 2646,541613 124,263654 0,734941811 15421980 2125 2661,610799 80,585867 0,743095129 18291981 2150 2676,771363 54,415746 0,762991929 19271982 1942 2614,679166 7,812569 0,746260143 20531983 1944 2614,514752 4,621775 0,740101574 19271984 2077 2646,927600 15,738205 0,768048058 19461985 2496 2759,217940 54,359059 0,847959262 20321986 2107 2728,569547 20,356078 0,761823749 21001987 2361 2804,591307 42,622351 0,801140962 20341988 2619 2891,563739 60,362383 0,850662198 21871989 2776 2919,709520 47,475742 0,909651413 25031990 2161 2865,109846 6,645576 0,757277594 22601991 1265 2602,571022 -101,028184 0,612091077 23011992 872 2367,896116 -154,486873 0,561220518 21281993 2335 2531,289862 -27,334625 0,917333331 20131994 2623 2739,644238 66,940975 0,877365187 18961995 2721 3027,245984 155,205284 0,784138492 17181996 2192 3078,787179 113,739648 0,651669407 17861997 2336 2881,340352 -10,734942 0,853373610 29291998 1973 2763,415942 -53,610729 0,779328926 25191999 2085 2785,415151 -23,366754 0,762780548 21252000 2354 2969,477729 59,604979 0,736306959 18002001 2756 2973,805214 37,493981 0,897404693 25852002 2993 3117,145717 79,832590 0,887835533 23472003 3532 3355,935995 143,415665 0,936590294 24392004 4010 3659,235903 207,369362 0,952037102 25772005 4090 3673,010641 129,931512 1,027078646 34702006 4365 3817,753526 135,856062 1,041139735 33762007 4219 3846,445523 92,990436 1,032750209 37032008 4766 4003,985554 118,810274 1,095003232 37502009 3810 3850,050304 9,712064 1,004589934 42342010 4228 3884,457440 19,590093 1,069520071 40192011 4484 3960,254874 42,073029 1,092450298 40322012 4623 3978,926545 32,712486 1,135123989 43832013 36002014 35912015 37272016 39132017 42552018 43472019 43462020 46432021 42932022 46052023 47402024 4962

A,M: (α=0,2, β=0,4, γ=0,6), L=4




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)




Table 3: Summary of the achieved forecast calculations though the use of Holt-Winters method

Ft+m, A,M, L=4

Ft+k, A,M L=8

Ft+m, A,M L=12

Ft+m, A,M L=20

Ft+m, A,M L=24

Ft+m, A,A L=4

Ft+m, A,A L=8

Ft+m, A,A L=12

Ft+m, A,A L=20

Ft+m, A,A L-24

2013 3600 3822 4695 6292 5514 3549 4324 4206 4180 42122014 3591 4018 4579 7045 6168 3572 4250 4269 4426 44472015 3727 4356 4092 6454 5565 3722 4049 4074 4301 43392016 3913 4583 3924 10077 8859 3988 4299 4522 5318 51422017 4255 4615 4863 10228 9993 4195 4103 4944 5549 52572018 4347 4285 5063 10043 10638 4306 4421 5143 5676 54742019 4346 3837 4920 10490 11202 4315 4709 5106 5752 54822020 4643 3847 4896 11857 14813 4649 4840 5279 6181 61182021 4293 3912 4454 11226 12366 4257 3323 4912 5835 57702022 4605 4259 4375 11175 11869 4548 3368 5045 6022 59512023 4740 4584 4601 9643 10041 4677 3413 5266 6032 59542024 4962 4754 5230 8549 8753 4906 3459 5504 6107 6021

YearTotal number of foreign visitors with recreation and holiday aims (in thousands)



3. METHODOLOGY AND MAIN RESULTS (13 of 18)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




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.




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.




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:

(12) ))(( 11 LmtttRTmtSWB SmTBKF ,

(13) )( 11 LmtttRTmtSWB SmTBKF

Where: KRT is the coefficient of the share of foreign visitors with aims to practice and experience tourism at the local tourism receiving market of South-West Bulgaria (Table 3).




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).




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:

The 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: FSWBT+12 = 0.1810 x 3 459 000 = 626 079 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: FetT+12 = 0.1810 x 6 107 000 = 1 105 367 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:

FetT+12 = 0.1810 x 8 753 000 = 1 584 293 foreign visitors.



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.

THANK YOU FOR YOUR ATTENTION!!!

Associate Prof. Dr. Preslav Dimitrov

South-West University “Neofit Rilski”, Blagoevgrad, Bulgaria

[email protected], [email protected]

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FORECASTING OF THE NUMBER OF THE TOURISM ARRIVALS IN SOUTH-WEST BULGARIA Associate Prof. Dr. Preslav Dimitrov Head of the LOFT Project Implementation team