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季节调整FWA-SVR模型及其在旅游经济预测中的应用

张婷婷1,王沫然2,魏得胜3,刘志峰4   

  1. 1. 海南大学经济学院, 海口 570228; 2. 东北师范大学罗格斯大学纽 瓦克学院, 长春 130117; 3. 西南财经大学金融学院, 成都 611130;4. 海南大学管理学院,海口 570228
  • 出版日期:2021-06-25 发布日期:2021-09-17

张婷婷, 王沫然, 魏得胜, 刘志峰. 季节调整FWA-SVR模型及其在旅游经济预测中的应用[J]. 系统科学与数学, 2021, 41(6): 1572-1584.

ZHANG Tingting, WANG Moran, WEI Desheng, LIU Zhifeng. Seasonally-Adjusted FWA-SVR Model and Its Application in Tourism Economic Forecast[J]. Journal of Systems Science and Mathematical Sciences, 2021, 41(6): 1572-1584.

Seasonally-Adjusted FWA-SVR Model and Its Application in Tourism Economic Forecast

ZHANG Tingting1 ,WANG Moran2 ,WEI Desheng3 ,LIU Zhifeng4   

  1. 1. School of Economics, Hainan University, Haikou 570228; 2. Rutgers University Newark Institute, Northeast Normal University, Changchun 130117; 3. School of Finance, Southwestern Univeriof Finance and Economics, Chengdu 611130; 4. Management School, Hainan University, Haikou 570228
  • Online:2021-06-25 Published:2021-09-17
引入一种全新的智能优化算法------烟花算法------对支持向 量回归模型中的参数选择过程进行优化, 并考虑旅游经济行为中的季节性 因素, 构建了季节调整的烟花算法支持向量回归模型(FWA-SVR).随后, 文章将该模型应用于海南国 际旅游岛的旅游过夜接待人数和旅游收入的预测中. 预测结果表明, 与不进行季节调整的ARMA模型相比, 季节调整后的FWA-SVR 模型具有更好的预测精度. 而与经典的遗传算法、粒子群算法相比, FWA-SVR模型在所有模型中的 预测表现也是最优的.
This paper introduces a new intelligent optimization algorithm, named Firework Algorithm (FWA), to optimize the parameter selection process in the Support Vector Regression model (SVR). And then, considering the seasonal factors in tourism economic behavior, we build a seasonally-adjusted FWA-SVR model and apply it to predict the number of overnight tourists and tourism revenue of Hainan International Tourism Island. The prediction results show that the FWA-SVR model after the seasonal adjustment has better prediction accuracy than the ARMA model without seasonal adjustment. Compared with the classic Genetic Algorithm and Particle Swarm Optimization Algorithm, the FWA-SVR model performs best in all prediction models.
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