Comparison of Near Neighbour and Neural Network in Travel Forecasting

被引:14
作者
Olmedo, Elena [1 ]
机构
[1] Univ Seville, Dept Econ Aplicada 1, Avda Ramon & Cajal 1, Seville 41018, Spain
关键词
forecasting; neural networks; reconstruction; travel; nonlinearity; TOURISM DEMAND; MEASUREMENT ERROR; TIME-SERIES; CHAOS; MODEL; ARRIVALS;
D O I
10.1002/for.2370
中图分类号
F [经济];
学科分类号
02 ;
摘要
In this paper we confirm the existence of nonlinear dynamics in a time series of airport arrivals. We subsequently propose alternative non-parametric forecasting techniques to be used in a travel forecasting problem, emphasizing the difference between the reconstruction and learning approach. We compare the results achieved in point prediction versus sign prediction. The reconstruction approach offers better results in sign prediction and the learning approach in point prediction. Copyright (c) 2015 John Wiley & Sons, Ltd.
引用
收藏
页码:217 / 223
页数:7
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