| چکیده انگلیسی مقاله |
IntroductionFor efficient organization and effective management of tourism and the pertinent activities,modeling and forecasting the tourist destination areas are vital issues for good performance. Ithelps make a better policy and plan for supplying tourist requirements. The number of tourists isrelated to the market supply and demand. Different services are cooperated in supplying tourismproductions, such as reception, entertainment, residential, health and information services. Onthe other hand, regarding demand, there are many factors affecting the tourists' destination. Forexample, economic-social conditions, language, culture and motivation that form the requestprocess tourists. Undoubtedly, demand prediction is a drastic factor especially for activitiesrelated to tourism. In one hand, manager and planners relevant to tourism make attempt to fulfilltourisms' demands. On the other hand, many of tourisms products like hotel's rooms, airplaneseats, rent car, museum or cultural plans are not being reserved or stored naturally. A hotel roomthat is not reserved for a night, an airplane seat that has no passenger and a restaurant table thatremains empty, are the benefits that have spoiled and they may not be reserved for the future.Therefore, the tourists demand shall be predicted. Alongside the prediction process and tourismentry demand model, the governments can organize their strategies better and prepareappropriate infrastructure for serving the tourists; the private sectors could make appropriate marketing strategies for obtaining the maximum benefits from tourist entry increase, as well.The forecasting of tourism demand is an essential tool for determining the required supply andthe appropriate distribution method of tourism services. When services (like tourism) achievedesirable market, its current amount and the future potential volume shall be estimatedprecisely. Market underestimation or overestimation makes the supplier lose the main part ofhis/her interest. Hence, planning and development of tourism require identifying such thesekinds of motivations and demands. Accordingly, what is vitally important for the tourismmanagement is the amount of accuracy of prediction model that led to development anddiversity of tools and new methods in prediction.MethodologyIn this article, the plan is to forecast the number of tourist arrival for the historical - cultural siteof Perspolis in south Iran. The time series involves monthly data that were collected for bothdomestic and international tourists. In order to testify the performance of forecasting method,the collected data were divided into two sets, training (Farvardin 1376- Esfand 1387) and testing(Farvardin1388- Esfand 1389). We used seasonal ARIMA model to detect the hidden structureof data and finally forecast the arrivals for both data sets.Results and DiscussionBased on the Box & Jenkins approach, both time series data were analyzed. In this approach,stationarity of time series is a preliminary condition. Therefore, before any attempts, the timeseries were made stationary by differencing.The result of data analysis of Persepolis- domestic tourismSince the number of visitors in Farvadin (April) of each year has considerable difference fromthe other menthes, therefore, it is likely that the forecasting model would be seasonal. The greatamount of autocorrelation function in the lags 12, 24 & 36 confirms the existence of theseasonal model. Since the seasonal data are not stationary, differencing can help to make asteady time series. The results showed that, seasonal differencing in order 12, and then firstdifferencing make the time series in an acceptable stationary form. Thus, we could determinethe seasonal model of ARIMA (p,1,q) (P,1,Q)12 according to the ACF and PACF of the finalseries. Exponential decay of PACF in some of the first lags (figure 3, right frame) and the factthat autocorrelation amount in lag 1,r1,is significantly different from zero, shows no seasonalmoving average model of order 1, MA(1), i.e. p=0, q=1. It is also observed in autocorrelationfunction (figure 3, left frame) that the amount of r24 is significant and this means a seasonal MA(2) (P=0, Q=2). Therefore, the final model of ARIMA (0,1,1) (01,2)12 may be written as thefollowing:1) 1 − 1 − 12 = 1 − 1 1 − 112 − 24 2) − 1 − 24 + 25 = − 11 − 112 + 1113 − 224 + 2125The result of data analysis on Persepolis- international tourismThe plot of this time series implies that it is non-stationary. However, seasonality is not obviousin the last example, but since the amount of r6 and r12 in autocorrelation diagram are located outof the 95% confidence interval, a seasonal differencing with a six-month course is suggested.The results show that the six-month seasonal differentiation series is not stationary, but if thisseries be re-differencing (first order) we may observe an approximately stationary series.In order to determine the order and the kind of series in non-seasonal part of ARIMA(p,1,q)(P,1Q)6, we could consider the amount of autocorrelation as an evidence of damping sinewave to zero and since the two first amount of partial autocorrelation are significant anddifferent from zero, the unseasonal autoregressive model, p=2, q=0, is suggested. In theseasonal part, (P,1,Q), r6, r12,r18,…., are damping to zero and since the amount of partialautocorrelation in lag 6 is significant, the seasonal AR model with Q=0 & P=1 seems to be moreappropriate. ARIMA (2,1,0)(1,1,0)6 is as the following.3) 1 − 1 − 221 − 61 − 1 − 6 = Evaluation of the suggested model was made by comparing real test data versus theforecasted data. Figures 5 and 9 successfully showed that both real and forecasted values oftourist arrival have the same variation in different months.ConclusionIn this research, we conclude that, the tourist arrival time series can be stationary by twodifferentiations (seasonal and first order differencing). In other words, the seasonal factor of thisseries is the inseparable part of them, with this difference that, the seasonal course for domesticand foreign visitors is 12 & 6 months, respectively. The results also show that the seasonalARIMA model is an appropriate estimation for forecasting the number of tourists. |