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Improved Grey Model by Dragonfly Algorithm for Chinese Tourism Demand Forecasting

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Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices (IEA/AIE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12144))

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Abstract

For Chinese tourism demand forecasting, we present a novel hybrid framework, a rolling grey model optimized by dragonfly algorithm (RGM-DA). In our framework, a rolling grey model is deployed to forecast the following demand, while the weight parameter in grey model is optimized by the dragonfly algorithm. Using the Experimental data from National Bureau of Statistics of China during 1994–2015, it shows our proposed framework is superior to all considered benchmark models with higher accuracy. Moreover, our proposed framework is a promising tool for short time series modelling.

This work was supported by the ARC Centre of Excellence for Mathematical and Statistical Frontiers.

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Acknowledgment

All the authors thank to Prof. You-Gan Wang and Prof. Yu-Chu Tian, QUT, for their kind suggestions to improve the quality of this work.

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Authors and Affiliations

  1. School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, 4001, Australia

    Jinran Wu

  2. School of Computer Science, Queensland University of Technology, Brisbane, QLD, 4001, Australia

    Zhe Ding

Authors
  1. Jinran Wu
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  2. Zhe Ding
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Corresponding author

Correspondence to Zhe Ding .

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Editors and Affiliations

  1. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

  2. Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  3. Texas State University, San Marcos, TX, USA

    Moonis Ali

  4. Iwate Prefectural University, Takizawa, Japan

    Jun Sasaki

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Wu, J., Ding, Z. (2020). Improved Grey Model by Dragonfly Algorithm for Chinese Tourism Demand Forecasting. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_18

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  • DOI: https://doi.org/10.1007/978-3-030-55789-8_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-55788-1

  • Online ISBN: 978-3-030-55789-8

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