Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network.

Clicks: 224
ID: 68747
2017
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Abstract
With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient.
Reference Key
yu2017statisticalcomputational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yu, Ying;Wang, Yirui;Gao, Shangce;Tang, Zheng;
Journal Computational Intelligence and Neuroscience
Year 2017
DOI
10.1155/2017/7436948
URL
Keywords
incomplete weight information linear program linguistic multi-attribute group decision making low-carbon tourism destination selection risk preference

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