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首页> 外文期刊>Expert systems with applications >A Temporal Case Retrieval Model To Predict Railway Passenger Arrivals
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A Temporal Case Retrieval Model To Predict Railway Passenger Arrivals

机译：预测铁路旅客到达的时间案例检索模型

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摘要

This paper proposes a three-stage model to predict final sales when advanced booking, which is prevalent in the service industry, is available. The concept behind the proposal is that similar booking patterns during the reservation period indicate the trend of sales. Booking curves which record accumulated reservations were collected from a railway company. The first stage is to evaluate the similarity of booking patterns between the collected samples and the days to be predicted. Then samples with high similarity to the forecasting target are chosen from the collected observations. Integrating the final sales of these selected samples to project future volumes is the main job in the last stage. Regression and Pick Up models, common in practice, are also constructed for comparing purposes. The results show that the proposed model can significantly improve predictive accuracy in the testing cases.
机译：本文提出了一个三阶段模型来预测服务业中普遍存在的高级预订时的最终销售额。该提案的概念是，在预订期内类似的预订模式表明了销售趋势。从铁路公司收集了记录累积预订量的预订曲线。第一步是评估所收集的样本与要预测的天数之间预订模式的相似性。然后从收集的观测值中选择与预测目标高度相似的样本。在最后阶段，主要工作是将这些选定样品的最终销售量与将来的销量进行整合。实际上，还建立了回归和拾取模型，用于比较目的。结果表明，该模型可以显着提高测试案例的预测准确性。

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