FORECASTING TOURISM MARKET DEMAND IN HUNAN PROVINCE USING ARIMA MODEL
DOI:
https://doi.org/10.31941/delta.v9i2.1410Keywords:
tourism market, time series, ARIMA modelAbstract
Hunan Province is a province with large tourism resources, and it’s tourism occupies a significant position in economic development. In recent years, Hunan province is in a critical period of transition from a major tourist province to a staggering tourist province. Therefore it is of great significance to analyze and predict the development trend of the future tourism market demand in Hunan Province and provide the government and tourism enterprise managers with the scientific market decision. Based on time-series analysis and forecasting theory, this paper attempts to use the time-series data of the number of domestic tourists in Hunan Province which from 2000 to 2019 and construct model autoregressive and integrated moving average (ARIMA) to predict the number of tourists in the Hunan Province in the next four years. The results show that the error between the predicted value and the true value is small. Therefore, the prediction effect of the model is well, which will provide a theoretical basis for the government to establish relevant policies. However, considering the characteristics of time-series data, the model needs to be further revised and even effectively combined with other models to gain better forecasting results.
References
a People’s Republic of China partnership. (2016). The 13th Five-Year Plan – China ’ s transformation and integration with the world economy. In KPMG Advisory (China) Limited. Retrieved from https://assets.kpmg.com/content/dam/kpmg/cn/pdf/en/2016/10/13fyp-opportunities-analysis-for-chinese-and-foreign-businesses.pdf
Ariyo A A, Adewumi A O, A. C. K. (2015). Stock Price Prediction Using the ARIMA Model. Uksim-Amss International Conference on Computer Modelling & Simulation.
Cho，V.A. (2003). Comparison of three different approaches to tourism arrival forecasting. Tourism Management, 24, 323–330.
Huang, J. jie, Xiong, M. zheng, Chen, Y., & Zhao., F. yan. (2020). E-commerce Product Sales Plan Based on LDA and ARIMA. International Journal of Computational and Engineering, 5(3).
Janardhanan, D., & Barrett, E. (2018). CPU workload forecasting of machines in data centers using LSTM recurrent neural networks and ARIMA models. Internet Technology & Secured Transactions. IEEE.
Kima, J. H., & Moosab, I. A. (2005). Forecasting international tourist flows to Australia:a comparison between the direct and indirect methods. Tourism Management, 26(2), 69–78.
Li, Q., Sun, X., Chen, C., & Yang, X. (2012). Characterizing the household energy consumption in heritage Nanjing Tulou buildings, China: A comparative field survey study. Energy and Buildings, 49, 317–326. https://doi.org/10.1016/j.enbuild.2012.02.023
Min, X., & Zhongfeng., Q. (2021). A novel hybrid ARIMA and regression tree model for the interval-valued time series. Journal of Statistical Computation and Simulation, 91(5).
Nunohara, K., Imafuku, R., Saiki, T., Bridges, S. M., Kawakami, C., Tsunekawa, K., … Suzuki, Y. (2020). How does video case-based learning influence clinical decision-making by midwifery students? An exploratory study. BMC Medical Education, 20(1), 1–10. https://doi.org/10.1186/s12909-020-1969-0
Rahmanian Vahid, Bokaie Saied, Haghdoost Aliakbar, B. M. (2020). Temporal analysis of visceral leishmaniasis between 2000 and 2019 in Ardabil Province, Iran: A time-series study using ARIMA model. Journal of Family Medicine and Primary Care, 9(12).
Seetanah B，Sannassee R，Rojid S. (2015). The impact of relative prices on tourism demand for Mauritius: An empirical analysis. Development Southern Africa.
Su, M. M., Wall, G., Wang, Y., & Jin, M. (2017). Multi-agency management of a World Heritage Site: Wulingyuan Scenic and Historic Interest Area, China. Current Issues in Tourism, 20(12), 1290–1309. https://doi.org/10.1080/13683500.2016.1261810
Thiruchelvam Loshini, Dass Sarat Chandra, Asirvadam Vijanth Sagayan, Daud Hanita, G. B. S. (2021). Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models. Scientific Reports, 11(1).
Tiantian, L., Yanqin, W., & Le., Z. (2021). A Traffic Interval Prediction Method Based on ARIMA. Journal of Physics: Conference Series, 1880(1).
Titus, C. M., Wanjoya, A., & Mageto, T. (2021). Time Series Modeling of Guinea Fowls Production in Kenya Using the ARIMA and ARFIMA Models. International Journal of Data Science and Analysis, 7(1).
Turner, L. W. A. W. (2011). Factors influencing demand for international tourism:tourism demand analysis using structural equation modeling，Revistied. Tourism Economics, 7, 21–38.
Wang, K., Wang, M., Gan, C., Chen, Q., & Voda, M. (2021). Tourism Economic Network Structural Characteristics of National Parks in the Central Region of China.
Zhuo, X. (2018). The Indigenous Traditions of Chinese Religions. https://doi.org/10.1007/978-981-10-6379-4_3
Zou, S. (2020). Research on GDP Forecast of Ji’an City Based on ARIMA Model. Open Journal of Social Sciences, 08(12).
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
