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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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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