Multi-model tourist forecasting: case study of Kurdistan Region of Iraq

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The tourism industry has been one of the leading service industries in the global economy in recent years and the number of international tourism in 2018 reached 1.4 billion. The goal of the research is to evaluate the performance of various methods for forecasting tourism data and predict the number of tourists during 2019 and 2022. Performance of 15 prediction models (i.e. Local linear structural, Naïve, Holt, Random walk, ARIMA) was compared. Based on error measurements matrix (i.e. RMSE, MAE, MAPE, MASE), the most accurate method was selected to forecast the total number of tourists from 2019 to 2022 to Kurdistan Region (KR), then forecasts were performed for each governorate in KR. The results show that among 15 examined models of tourist forecasting in KR, Local linear structural and ARIMA (7,3,0) model performed best. The number of tourists to KR and each governorate in KR is predicted to increase by most experimented models, especially those which demonstrated higher accuracy. Generally, the number of tourist to KR predicted by ARIMA (7,3,0) is a lot bigger than Local linear structure. Linear structural predicted the number increase to 3,137,618 and 3,462,348 in 2020 and 2022, respectively, while ARIMA (7,3,0) predicted the number of tourists to KR to increase rapidly to 3,748,416 and 8,681,398 in 2020 and 2022.

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    • Figure 1. Total tourist to KR (2007–2018) along with 4-year forecasts and 80% and 95% prediction intervals
    • Figure 2. Total tourist to Erbil Governorate (2007–2018) along with 4-year forecasts and 80% and 95% prediction intervals
    • Figure 3. Total tourist to Sulaymaniyah Governorate (2007–2018) along with 4-year forecasts and 80% and 95% prediction intervals
    • Figure 4. Total tourist to Duhok Governorate (2007–2018) along with 4-year forecasts and 80% and 95% prediction intervals
    • Table 1. Accuracy assessment of tourist forecasting models
    • Table 2. Forecasted number of tourists to KR from Local linear structural
    • Table 3. Forecasted number of tourists to KR from ARIMA (7,3,0)
    • Table 4. Forecasted number of tourists to Erbil Governorate from Local linear structural model
    • Table 5. Forecasted number of tourists to Erbil Governorate from ARIMA (7,3,0)
    • Table 6. Forecasted number of tourists to Sulaymaniyah Governorate from Local linear structural model
    • Table 7. Forecasted number of tourists to Duhok Governorate from Local linear structural model
    • Table 8. Forecasted number of tourists to Duhok Governorate from ARIMA (7,3,1)