Research Article
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Forecasting Tourist Arrivals to Sangiran Using Fuzzy with Calendar Variations

Year 2022, Volume: 10 Issue: 4, 605 - 624, 06.12.2022
https://doi.org/10.30519/ahtr.990903

Abstract

Fuzzy method has been widely used in time series forecasting. However, the current fuzzy time models have not accommodated the holiday effects so that the forecasting error becomes large at certain moments. Regarding the problem, this study proposes two algorithms, extended of Chen’s and seasonal fuzzy time series method (FTS), to consider the holiday effect in forecasting the monthly tourist arrivals to ancient human Sangiran Museum. Both algorithms consider the relationship between Eid holidays as the effect of calendar variations. The forecasting results obtained from the two proposed algorithms are then compared with those obtained from the Chen’s and the seasonal FTS. Based on the experimental results, the proposed method can reduce mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) obtained from Chen’s method up to 61%, 61%, and 58%, respectively. Moreover, compared to that obtained from the seasonal FTS, the proposed method can reduce the MAE, RMSE, and MAPE values up to 35%, 36%, and 29%, respectively. The method proposed in this paper can be implemented to other time series with seasonal pattern and calendar variation effects.

References

  • Aladag, S., Aladag, C. H., Mentes, T., & Egrioglu, E. (2012). A new seasonal fuzzy time series method based on the multiplicative neuron model and SARIMA. Hacettepe Journal of Mathematics and Statistics, 41(3), 337–345.
  • Alpaslan, F., Cagcag, O., Aladag, C. H., Yolcu, U., & Egrioglu, E. (2012). A novel seasonal fuzzy time series method. Hacettepe Journal of Mathematics and Statistics, 41(3), 375–385.
  • Anggraeni, W., Vinarti, R. A., & Kurniawati, Y. D. (2015). Performance comparisons between arima and arimax method in moslem kids clothes demand forecasting: Case study. Procedia Computer Science, 72, 630–637.
  • Bas, E., Yolcu, U., & Egrioglu, E. (2021). Intuitionistic fuzzy time series functions approach for time series forecasting. Granular Computing, 6(3), 619-629.
  • Cagcag, O., Yolcu, U., Egrioglu, E., & Aladag, C. H. (2013). A novel seasonal fuzzy time series method to the forecasting of air pollution data in Ankara. American Journal of Intelligent Systems, 3(1), 13–19.
  • Chang, Y. W., & Liao, M. Y. (2010). A seasonal ARIMA model of tourism forecasting: The case of Taiwan. Asia Pacific Journal of Tourism Research, 15(2), 215–221.
  • Chen, K. Y. (2011). Combining linear and nonlinear model in forecasting tourism demand. Expert Systems with Applications, 38(8), 10368–10376. https://doi.org/10.1016/j.eswa.2011.02.049
  • Chen, R. J., Bloomfield, P., & Cubbage, F. W. (2008). Comparing forecasting models in tourism. Journal of Hospitality & Tourism Research, 32(1), 3–21.
  • Chen, S. M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81(3), 311–319.
  • Cheng, C. H., Chen, T. L., Teoh, H. J., & Chiang, C. H. (2008). Fuzzy time-series based on adaptive expectation model for TAIEX forecasting. Expert Systems with Applications, 34(2), 1126–1132.
  • Egrioglu, E., Bas, E., Yolcu, U., & Chen, M. Y. (2020). Picture fuzzy time series: Defining, modeling and creating a new forecasting method. Engineering Applications of Artificial Intelligence, 88, 103367.
  • Gao, R., & Duru, O. (2020). Parsimonious fuzzy time series modelling. Expert Systems with Applications, 156, 113447.
  • Hanke, E.J., Wichern, W. D., & Reitsch, G. A. (2005). Business Forecasting (8th ed.). Pearson, Prentice Hall.
  • Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001
  • Koo, J. W., Wong, S. W., Selvachandran, G., & Long, H. V. (2020). Prediction of Air Pollution Index in Kuala Lumpur using fuzzy time series and statistical models. Air Quality, Atmosphere & Health, 13(1), 77–88.
  • Lee, M. H., & Hamzah, N. (2010). Calendar variation model based on ARIMAX for forecasting sales data with Ramadhan effect. Proceedings of the Regional Conference on Statistical Sciences, 10, 30–41.
  • Lee, M. H., & Javedani, H. (2011). A weighted fuzzy integrated time series for forecasting tourist arrivals. International Conference on Informatics Engineering and Information Science, 206–217.
  • Lee, M. H., Nor, M. E., Suhartono, Sadaei, H. J., Rahman, N. H. A., & Kamisan, N. A. B. (2012). Fuzzy Time Series: An Application to Tourism Demand Forecasting. American Journal of Applied Sciences, 9(1), 132–140. https://doi.org/10.3844/ajassp.2012.132.140
  • Lee, M. H., & Suhartono. (2010). A novel weighted fuzzy time series model for forecasting seasonal data. Proceeding the 2nd International Conference on Mathematical Sciences, 332–340.
  • Ling, A. S. C., Darmesah, G., Chong, K. P., & Ho, C. M. (2019). Application of ARIMAX Model to Forecast Weekly Cocoa Black Pod Disease Incidence. Mathematics and Statistics, 7(4A), 29–40. https://doi.org/10.13189/ms.2019.070705
  • Liu, H. T., & Wei, M. L. (2010). An improved fuzzy forecasting method for seasonal time series. Expert Systems with Applications, 37(9), 6310–6318.
  • Makridakis, S., & Hibon, M. (2000). The M3-Competition: Results, conclusions and implications. International Journal of Forecasting, 16(4), 451–476. https://doi.org/10.1016/S0169-2070(00)00057-1
  • Sarı, I. U. (2012). Forecasting energy demand using fuzzy seasonal time series. In Computational Intelligence Systems in Industrial Engineering (pp. 251–269). Springer.
  • Singh, H., Gupta, M. M., Meitzler, T., Hou, Z. G., Garg, K. K., Solo, A. M. G., & Zadeh, L. A. (2013). Real-Life Applications of Fuzzy Logic. Advances in Fuzzy Systems, 2013, e581879. https://doi.org/10.1155/2013/581879
  • Song, Q. (1999). Seasonal forecasting in fuzzy time series. Fuzzy Sets and Systems, 107(2), 235–236.
  • Song, Q., & Chissom, B. S. (1993). Forecasting enrollments with fuzzy time series—Part I. Fuzzy Sets and Systems, 54(1), 1–9.
  • Song, Q., & Chissom, B. S. (1993). Fuzzy time series and its models. Fuzzy Sets and Systems, 54(3), 269–277.
  • Song, Q., & Chissom, B. S. (1994). Forecasting enrollments with fuzzy time series—Part II. Fuzzy Sets and Systems, 62(1), 1–8.
  • Suhartono, Lee, M. H., & Prastyo, D. D. (2015). Two levels ARIMAX and regression models for forecasting time series data with calendar variation effects. AIP Conference Proceedings, 1691(1), 050026.
  • Suhartono, S. (2006). Calendar variation model for forecasting time series data with islamic calendar effect. Jurnal Matematika Sains Dan Teknologi, 7(2), 85–94.
  • Suhartono, S., Dana, I. M. G. M., & Rahayu, S. P. (2019). Hybrid model for forecasting space-time data with calendar variation effects. Telkomnika, 17(1), 118–130. https://doi.org/10.12928/TELKOMNIKA.v17i1.10096
  • Sulandari, W., Subanar, S., Lee, M. H., & Rodrigues, P. C. (2020). Time series forecasting using singular spectrum analysis, fuzzy systems and neural networks. MethodsX, 7, 101015. https://doi.org/10.1016/j.mex.2020.101015
  • Sulandari, W., Subanar, S., Suhartono, S., Utami, H., Lee, M. H., & Rodrigues, P. C. (2020). SSA-based hybrid forecasting models and applications. Bulletin of Electrical Engineering and Informatics, 9(5), 2178–2188. https://doi.org/10.11591/eei.v10i1.1950
  • Sulandari, W., Subanti, S., Slamet, I., Sugiyanto, Zukhronah, E., & Susanto, I. (2021). Application of linear and nonlinear seasonal autoregressive based methods for forecasting Grojogan Sewu tourism demand. AIP Conference Proceedings, 2329(1), 060008.
  • Sumarminingsih, E., Matoha, S., Suharsono, A., & Ruchjana, B. N. (2018). Spatial Vector Autoregressive Model with Calendar Variation for East Java Inflation and Money Supply. Appl. Math. Inf. Sci, 12(6), 1157–1163.
  • Sun, S., Wei, Y., Tsui, K.-L., & Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, 1–10. https://doi.org/10.1016/j.tourman.2018.07.010
  • Tayyaba, S., Ashraf, M. W., Alquthami, T., Ahmad, Z., & Manzoor, S. (2020). Fuzzy-Based Approach Using IoT Devices for Smart Home to Assist Blind People for Navigation. Sensors (Basel, Switzerland), 20(13), 3674. https://doi.org/10.3390/s20133674
  • Vlamou, E., & Papadopoulos, B. (2019). Fuzzy logic systems and medical applications. AIMS Neuroscience, 6(4), 266–272. https://doi.org/10.3934/Neuroscience.2019.4.266
  • Wong, K. K., Song, H., Witt, S. F., & Wu, D. C. (2007). Tourism forecasting: To combine or not to combine? Tourism Management, 28(4), 1068–1078.
  • Yu, H. K. (2005). Weighted fuzzy time series models for TAIEX forecasting. Physica A: Statistical Mechanics and Its Applications, 349(3–4), 609–624.
Year 2022, Volume: 10 Issue: 4, 605 - 624, 06.12.2022
https://doi.org/10.30519/ahtr.990903

Abstract

References

  • Aladag, S., Aladag, C. H., Mentes, T., & Egrioglu, E. (2012). A new seasonal fuzzy time series method based on the multiplicative neuron model and SARIMA. Hacettepe Journal of Mathematics and Statistics, 41(3), 337–345.
  • Alpaslan, F., Cagcag, O., Aladag, C. H., Yolcu, U., & Egrioglu, E. (2012). A novel seasonal fuzzy time series method. Hacettepe Journal of Mathematics and Statistics, 41(3), 375–385.
  • Anggraeni, W., Vinarti, R. A., & Kurniawati, Y. D. (2015). Performance comparisons between arima and arimax method in moslem kids clothes demand forecasting: Case study. Procedia Computer Science, 72, 630–637.
  • Bas, E., Yolcu, U., & Egrioglu, E. (2021). Intuitionistic fuzzy time series functions approach for time series forecasting. Granular Computing, 6(3), 619-629.
  • Cagcag, O., Yolcu, U., Egrioglu, E., & Aladag, C. H. (2013). A novel seasonal fuzzy time series method to the forecasting of air pollution data in Ankara. American Journal of Intelligent Systems, 3(1), 13–19.
  • Chang, Y. W., & Liao, M. Y. (2010). A seasonal ARIMA model of tourism forecasting: The case of Taiwan. Asia Pacific Journal of Tourism Research, 15(2), 215–221.
  • Chen, K. Y. (2011). Combining linear and nonlinear model in forecasting tourism demand. Expert Systems with Applications, 38(8), 10368–10376. https://doi.org/10.1016/j.eswa.2011.02.049
  • Chen, R. J., Bloomfield, P., & Cubbage, F. W. (2008). Comparing forecasting models in tourism. Journal of Hospitality & Tourism Research, 32(1), 3–21.
  • Chen, S. M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81(3), 311–319.
  • Cheng, C. H., Chen, T. L., Teoh, H. J., & Chiang, C. H. (2008). Fuzzy time-series based on adaptive expectation model for TAIEX forecasting. Expert Systems with Applications, 34(2), 1126–1132.
  • Egrioglu, E., Bas, E., Yolcu, U., & Chen, M. Y. (2020). Picture fuzzy time series: Defining, modeling and creating a new forecasting method. Engineering Applications of Artificial Intelligence, 88, 103367.
  • Gao, R., & Duru, O. (2020). Parsimonious fuzzy time series modelling. Expert Systems with Applications, 156, 113447.
  • Hanke, E.J., Wichern, W. D., & Reitsch, G. A. (2005). Business Forecasting (8th ed.). Pearson, Prentice Hall.
  • Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001
  • Koo, J. W., Wong, S. W., Selvachandran, G., & Long, H. V. (2020). Prediction of Air Pollution Index in Kuala Lumpur using fuzzy time series and statistical models. Air Quality, Atmosphere & Health, 13(1), 77–88.
  • Lee, M. H., & Hamzah, N. (2010). Calendar variation model based on ARIMAX for forecasting sales data with Ramadhan effect. Proceedings of the Regional Conference on Statistical Sciences, 10, 30–41.
  • Lee, M. H., & Javedani, H. (2011). A weighted fuzzy integrated time series for forecasting tourist arrivals. International Conference on Informatics Engineering and Information Science, 206–217.
  • Lee, M. H., Nor, M. E., Suhartono, Sadaei, H. J., Rahman, N. H. A., & Kamisan, N. A. B. (2012). Fuzzy Time Series: An Application to Tourism Demand Forecasting. American Journal of Applied Sciences, 9(1), 132–140. https://doi.org/10.3844/ajassp.2012.132.140
  • Lee, M. H., & Suhartono. (2010). A novel weighted fuzzy time series model for forecasting seasonal data. Proceeding the 2nd International Conference on Mathematical Sciences, 332–340.
  • Ling, A. S. C., Darmesah, G., Chong, K. P., & Ho, C. M. (2019). Application of ARIMAX Model to Forecast Weekly Cocoa Black Pod Disease Incidence. Mathematics and Statistics, 7(4A), 29–40. https://doi.org/10.13189/ms.2019.070705
  • Liu, H. T., & Wei, M. L. (2010). An improved fuzzy forecasting method for seasonal time series. Expert Systems with Applications, 37(9), 6310–6318.
  • Makridakis, S., & Hibon, M. (2000). The M3-Competition: Results, conclusions and implications. International Journal of Forecasting, 16(4), 451–476. https://doi.org/10.1016/S0169-2070(00)00057-1
  • Sarı, I. U. (2012). Forecasting energy demand using fuzzy seasonal time series. In Computational Intelligence Systems in Industrial Engineering (pp. 251–269). Springer.
  • Singh, H., Gupta, M. M., Meitzler, T., Hou, Z. G., Garg, K. K., Solo, A. M. G., & Zadeh, L. A. (2013). Real-Life Applications of Fuzzy Logic. Advances in Fuzzy Systems, 2013, e581879. https://doi.org/10.1155/2013/581879
  • Song, Q. (1999). Seasonal forecasting in fuzzy time series. Fuzzy Sets and Systems, 107(2), 235–236.
  • Song, Q., & Chissom, B. S. (1993). Forecasting enrollments with fuzzy time series—Part I. Fuzzy Sets and Systems, 54(1), 1–9.
  • Song, Q., & Chissom, B. S. (1993). Fuzzy time series and its models. Fuzzy Sets and Systems, 54(3), 269–277.
  • Song, Q., & Chissom, B. S. (1994). Forecasting enrollments with fuzzy time series—Part II. Fuzzy Sets and Systems, 62(1), 1–8.
  • Suhartono, Lee, M. H., & Prastyo, D. D. (2015). Two levels ARIMAX and regression models for forecasting time series data with calendar variation effects. AIP Conference Proceedings, 1691(1), 050026.
  • Suhartono, S. (2006). Calendar variation model for forecasting time series data with islamic calendar effect. Jurnal Matematika Sains Dan Teknologi, 7(2), 85–94.
  • Suhartono, S., Dana, I. M. G. M., & Rahayu, S. P. (2019). Hybrid model for forecasting space-time data with calendar variation effects. Telkomnika, 17(1), 118–130. https://doi.org/10.12928/TELKOMNIKA.v17i1.10096
  • Sulandari, W., Subanar, S., Lee, M. H., & Rodrigues, P. C. (2020). Time series forecasting using singular spectrum analysis, fuzzy systems and neural networks. MethodsX, 7, 101015. https://doi.org/10.1016/j.mex.2020.101015
  • Sulandari, W., Subanar, S., Suhartono, S., Utami, H., Lee, M. H., & Rodrigues, P. C. (2020). SSA-based hybrid forecasting models and applications. Bulletin of Electrical Engineering and Informatics, 9(5), 2178–2188. https://doi.org/10.11591/eei.v10i1.1950
  • Sulandari, W., Subanti, S., Slamet, I., Sugiyanto, Zukhronah, E., & Susanto, I. (2021). Application of linear and nonlinear seasonal autoregressive based methods for forecasting Grojogan Sewu tourism demand. AIP Conference Proceedings, 2329(1), 060008.
  • Sumarminingsih, E., Matoha, S., Suharsono, A., & Ruchjana, B. N. (2018). Spatial Vector Autoregressive Model with Calendar Variation for East Java Inflation and Money Supply. Appl. Math. Inf. Sci, 12(6), 1157–1163.
  • Sun, S., Wei, Y., Tsui, K.-L., & Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, 1–10. https://doi.org/10.1016/j.tourman.2018.07.010
  • Tayyaba, S., Ashraf, M. W., Alquthami, T., Ahmad, Z., & Manzoor, S. (2020). Fuzzy-Based Approach Using IoT Devices for Smart Home to Assist Blind People for Navigation. Sensors (Basel, Switzerland), 20(13), 3674. https://doi.org/10.3390/s20133674
  • Vlamou, E., & Papadopoulos, B. (2019). Fuzzy logic systems and medical applications. AIMS Neuroscience, 6(4), 266–272. https://doi.org/10.3934/Neuroscience.2019.4.266
  • Wong, K. K., Song, H., Witt, S. F., & Wu, D. C. (2007). Tourism forecasting: To combine or not to combine? Tourism Management, 28(4), 1068–1078.
  • Yu, H. K. (2005). Weighted fuzzy time series models for TAIEX forecasting. Physica A: Statistical Mechanics and Its Applications, 349(3–4), 609–624.
There are 40 citations in total.

Details

Primary Language English
Subjects Tourism (Other)
Journal Section Research Article
Authors

Wınıta Sulandari 0000-0002-8185-1274

Yudho Yudhanto This is me 0000-0001-8998-8577

Sri Subanti This is me 0000-0002-2493-4583

Etik Zukhronah This is me 0000-0001-6387-4483

Subanar Subanar This is me 0000-0001-7147-4471

Muhammad Hisyam Lee 0000-0002-3700-2363

Publication Date December 6, 2022
Submission Date September 4, 2021
Published in Issue Year 2022 Volume: 10 Issue: 4

Cite

APA Sulandari, W., Yudhanto, Y., Subanti, S., Zukhronah, E., et al. (2022). Forecasting Tourist Arrivals to Sangiran Using Fuzzy with Calendar Variations. Advances in Hospitality and Tourism Research (AHTR), 10(4), 605-624. https://doi.org/10.30519/ahtr.990903


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