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Forecasting Tourist Arrivals in China Based on Seasonal Decomposition and LSSVR Model

  • Gang XieEmail author
  • Jian Zhang
  • Boyu Yang
  • Shouyang Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)

Abstract

In this study, a novel decomposition-ensemble methodology is proposed based on seasonal decomposition and the least squares support vector regression (LSSVR) model for forecasting foreign tourist arrivals in China. Firstly, the original time series of foreign tourist arrivals in China are decomposed into several components. Then, an individual forecasting model is selected for the prediction of a component. Finally, these prediction results of the components are combined as an aggregated output of forecasts of foreign tourist arrivals. Using the time series of tourist arrivals from twelve foreign countries to China, an empirical study is implemented for the purposes of illustration and verification. The results suggest that our proposed hybrid model TS-SL-LSSVR can achieve better forecasting performance than other models such as seasonal autoregressive integrated moving average (SARIMA), back propagation neural network (BPNN), and single LSSVR model.

Keywords

Foreign tourist arrivals Seasonal decomposition Time series forecasting Hybrid model 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 71771207, 71642006), and the National Center for Mathematics and Interdisciplinary Sciences, CAS.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Gang Xie
    • 1
    Email author
  • Jian Zhang
    • 1
    • 2
  • Boyu Yang
    • 1
    • 2
  • Shouyang Wang
    • 1
  1. 1.CFS, MDIS, Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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