A Preliminary Study of Forecast Models for International Taiwan Inbound Tourism
Date Issued
2014
Date
2014
Author(s)
Chen, Chuan-Pu
Abstract
With the rapid growth of Taiwan''s economy, tourism has become an important new areas of national development and comprehensive stimulate national economic development. Tourism population number has exceeded the annual peak year by year. With this trend, the tourism industry also forth to meet a new development phase. As long as attracting foreign tourists, increasing demand for tourism services provide a significant contribution for national economic development and trade performance. Therefore, to understand what variables will affect tourism demand and how to apply variables to predict international tourist arrivals to Taiwan, giving decision-makers information to make strategic planning is very important.
Artificial intelligence technology has become an important tool for economic modeling and forecasting. With the development of information technology, several new technologies have been applied in travel demand forecasting. However, not an optimal model can be applied uniformly and absolutely superior to other models in multiple scenarios. This research discussed the accuracy and applicability of predictive models in back propagation network and support vector regression model for international tourism demand forecasting.
It is hoped that this research could provide a better understanding about tourism demand forecasting and give the tourism industry policy recommendations.
Artificial intelligence technology has become an important tool for economic modeling and forecasting. With the development of information technology, several new technologies have been applied in travel demand forecasting. However, not an optimal model can be applied uniformly and absolutely superior to other models in multiple scenarios. This research discussed the accuracy and applicability of predictive models in back propagation network and support vector regression model for international tourism demand forecasting.
It is hoped that this research could provide a better understanding about tourism demand forecasting and give the tourism industry policy recommendations.
Subjects
旅遊需求預測
類神經網路
倒傳遞神經網路
支撐向量回歸
SDGs
Type
thesis
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