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首页> 外文期刊>Annals of Operations Research >Forecasting tourism demand using fractional grey prediction models with Fourier series
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Forecasting tourism demand using fractional grey prediction models with Fourier series

机译：使用傅里叶系列的分数灰色预测模型预测旅游需求

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摘要

Tourism demand forecasting has played an important role in supporting governments to devise development policies for travel and tourism. However, time series related to tourism often do not conform to statistical assumptions and feature significant temporal fluctuations. Because a Fourier series is often applied to oscillating sequences to remove noise, it is reasonable to develop a grey prediction model in conjunction with a Fourier series to forecast tourism demand. However, grey prediction models traditionally use one-order accumulation, treating each sample with equal weight, to identify regularities concealed in data sequences. Furthermore, when generating residuals from Fourier series, the prediction accuracy of the newly generated predicted values is not taken into account. In this study, by using fractional order accumulation to assign appropriate weights to samples, we propose a fractional grey prediction model with Fourier series that offers high prediction accuracy. Experimental results demonstrate that the proposed grey prediction model performs well compared with other considered prediction models.
机译：旅游需求预测在支持各国政府制定旅游和旅游业的发展政策方面发挥了重要作用。然而，与旅游相关的时间序列通常不符合统计假设，并具有显着的时间波动。因为傅里叶系列通常被应用于振荡序列以消除噪声，所以可以与傅里叶地系列一起开发灰色预测模型是合理的，以预测旅游需求。然而，灰色预测模型传统上使用单阶累积，以相等的重量处理每个样本，以识别隐藏在数据序列中的规律性。此外，当从傅里叶系列产生残差时，不考虑新生成的预测值的预测精度。在本研究中，通过使用分数累积将适当的权重分配给样本，我们提出了一种具有傅立叶系列的分数灰色预测模型，可提供高预测精度。实验结果表明，与其他考虑的预测模型相比，所提出的灰色预测模型良好。

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