Modeling of time series data for forecasting the number of foreign tourists in east Kalimantan using fuzzy inference system based on ARX model

  • Abstract
  • Keywords
  • References
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  • Abstract

    The government agencies require accurate tourism demand forecasts to plan the required tourism infrastructure, such as accommodation location planning and transportation development. Tourism demand forecasts can be viewed from various factors, one of which is the number of tourists per period. Without ignoring the number of domestic tourists, the increasing number of foreign tourists is prioritized by the government to increase the country's foreign exchange. Usually, a tourist destination is to visit some tour objects and need some accommodation and hotel sites to rest. With this consideration, the forecasting number of foreign tourists can be done by using data on the number of tour objects, accommodation and hotel sites, foreign and domestic tourists from the previous period. Data on the number of domestic tourists used to measure the tendency of foreign tourists compared with domestic tourists to all existing tour objects. All data history can be viewed as time series data. Conventionally, many researchers have employed traditional methods of time series analysis, modeling, and forecasting such as ARX (Autoregressive with exogenous input) model. FIS (Fuzzy Inference System) is a system that processes the input mapping formulation provided to produce output using Fuzzy Logic. The aim of this study is to forecast the number of foreign tourists by using FIS through a training process conducted by adapting the number of linguistic variables. All the training data are modeled by using the ARX model.

  • Keywords

    foreign tourist, domestic tourist, ARX model, FIS.

  • References

      [1] B. Statistics, East Kalimantan Province in Figures 2006-2017. 2017.

      [2] Y. Wang, “The Tourism Demand of Nonlinear Combination Forecasting based on Time Series Method and WNN,” Int. J. u- e-Service, Sci. Technol., vol. 8, no. 3, pp. 301–310, 2015.

      [3] O. Claveria and S. Torra, “Forecasting tourism demand to Catalonia: Neural networks vs. time series models,” Econ. Model., vol. 36, no. September 2013, pp. 220–228, 2014.

      [4] M. Gan, Y. Cheng, K. Liu, and G. L. Zhang, “Seasonal and trend time series forecasting based on a quasi-linear autoregressive model,” Appl. Soft Comput. J., vol. 24, no. November, pp. 13–18, 2014.

      [5] B. Prasad and K. Molugaram, “Development of mode choice models of a trip maker for Hyderabad metropolitan city,” Int. J. Eng. Technol., vol. 7, pp. 1–7, 2018.

      [6] A. Rahman and A. S. Ahmar, “Forecasting of primary energy consumption data in the United States: A comparison between ARIMA and Holter-Winters models,” in AIP Conference Proceedings, 2017, vol. 1885.

      [7] A. S. Ahmar et al., “Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO),” J. Phys. Conf. Ser., vol. 954, 2018.

      [8] A. S. Ahmar, “A Comparison of α-Sutte Indicator and ARIMA Methods in Renewable Energy Forecasting in Indonesia,” Int. J. Eng. Technol., vol. 7, no. 1.6, pp. 9–11, 2018.

      [9] M. H. Lee, M. E. Nor, H. J. Sadaei, N. H. A. Rahman, and N. A. B. Kamisan, “Fuzzy Time Series: An Application to Tourism Demand ForecastingNo Title,” Am. J. Appl. Sci., vol. 9, no. 1, pp. 132–140, 2012.

      [10] A. Tale, A. S. Gusain, J. Baguli, R. Sheikh, and A. Badar, “Study of Load Forecasting Techniques using,” Int. J. Advenced Res. Electr. Electron. Instrum. Eng., vol. 6, no. 2, pp. 510–518, 2017.

      [11] A. Cankurt, Selcuk Subasi, “Comparison of Linear Regression and Neural Network Models Forecasting Tourist Arrivals to Turkey,” Eurasian J. Sci. Eng., vol. 1, no. 1, pp. 21–25, 2015.

      [12] A. E. Abow Mohammed, “Using analysis of time series to forecast the number of patients with tuberculosis: a case study in Khartoum state from 2007 to 2016,” Int. J. Adv. Stat. Probab., vol. 6, no. 1, p. 24, 2018.

      [13] S. Rachad, B. Nsiri, and B. Bensassi, “System identification of inventory system using ARX and ARMAX models,” Int. J. Control Autom., vol. 8, no. 12, pp. 283–294, 2015.

      [14] H. N. Akouemo and R. J. Povinelli, “Data Improving in Time Series Using ARX and ANN Models,” IEEE Trans. Power Syst., vol. 32, no. 5, pp. 3352–3359, 2017.

      [15] Y. Guo, E. Nazarian, J. Ko, and K. Rajurkar, “Hourly cooling load forecasting using time-indexed ARX models with two-stage weighted least squares regression,” Energy Convers. Manag., vol. 80, no. April, pp. 46–53, 2014.

      [16] S. S. M. Khalifa, K. Saadan, and N. M. Norwawi, “Risk Assessment of Mined Areas using Fuzzy Inference,” Int. J. Artif. Intell. Appl., vol. 6, no. 118, pp. 37–51, 2015.

      [17] T. M. Inc, “Fuzzy Logic ToolboxTM User’s Guide,” The Mathworks, Inc, 2014. .




Article ID: 12745
DOI: 10.14419/ijet.v7i2.2.12745

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