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System No. U0002-2007201023045400
Title (in Chinese) 以模糊時間序列模式預測日本來台旅遊人數
Title (in English) The tourism demand forecasting using a novel high-precision fuzzy time series model for the Japanese to Taiwan
Other Title
Institution 淡江大學
Department (in Chinese) 管理科學研究所碩士班
Department (in English) Graduate Institute of Management Science
Other Division
Other Division Name
Other Department/Institution
Academic Year 98
Semester 2
PublicationYear 99
Author's name (in Chinese) 郭亭君
Author's name(in English) Ting-Chun Kuo
Student ID 697620564
Degree 碩士
Language Traditional Chinese
Other Language
Date of Oral Defense 2010-06-23
Pagination 69page
Committee Member advisor - Ruey-Chyn Tsaur
co-chair - Ching-Chun Fu
co-chair - 陳怡妃
Keyword (inChinese) 模糊時間序列
適應性模糊時間序列
傅立葉級數
旅遊人數預測
預測
模糊邏輯關係組
殘差分析
Keyword (in English) Fuzzy time series model
Adaptive fuzzy time series model
Fourier series
residual analysis
forecasting
tourism forecasting
fuzzy logic group
Other Keywords
Subject
Abstract (in Chinese)
觀光業帶來的商機,給民眾帶來更多的財富,觀光活動也可讓民眾在工作之餘,從事休閒育樂活動,平衡忙碌的生活。觀光產業還可以與自然資源、商務活動結合,進一步透過觀光交流,展現一國的人文內涵、經濟實力及基礎建設現代化程度,對改善整體環境、提升國家文化素質均助益匪淺。
    日本一直都是台灣在旅遊市場中最重要客源,透過正確的分析和完善的規劃與管理才能使旅遊市場的供需達到均衡。因此,必須準確地預測來台觀光旅客需求,才以掌握旅遊市場狀況與發展,以進一步規劃各種軟硬體設施的投資,例如:大規模飯店興建、遊覽車購置、導遊培訓…等;反之,不適當的評估或是不精確的預測,將導致觀光資源不敷使用或閒置浪費。   
由於統計迴歸模式必需收集完整的變數以建構預測模式,當收集的資料受到限制,時間序列資料存在語意值或是資料量少於50筆時,統計時間序列通常會因為無法得到較小的誤差而失效。為了處理這樣的問題,本研究首先提出採用前期資料的適應性模糊時間序列模式進行分析,結果顯示其績效不佳,且模式無法行進外差的預測。為改善此項缺點,本研究再提出一個具殘差修正的模糊時間序列模式,來處理外差預測的問題,預測結果顯示,無論是內差或外差,其MAPE與RMSE皆相當準確。
Abstract (in English)
Over the past few decades, the tourism industry has been grown very fast. Because of the tourism activity may for the country creation traveling income, plan and the management sightseeing resources because of the forecasting result. Thus, it is very important for planning for potential tourism demand and improving the tourism infrastructure, since accurate forecasting of tourist arrivals. Japan has been the most important source that Taiwan travels all the time. But the international exchange is frequent day by day, the competition of the tour undertaking is fiercer and fiercer, only depend on correct decision and planning and management of perfection.
There are many method can forecast, but when the collected are not enough to model regression model or time series model, or there exist fuzzy time series data, the statistical quantitative methods are usually failure to have smaller forecasting error. In order to provide a much more flexible examination for managing smaller data set or fuzzy data.
In this study, we proposed an adaptive fuzzy time series model for forecasting tourism demand for Japanese to Taiwan. But it can’t forecast accurately, and it can’t forecast about untrained data, so we proposed a new method which combined Fourier series with fuzzy time series for forecasting Japanese tourism demand for Taiwan, and obtained very small forecasting error MAPE and RMSE.
Other Abstract
Table of Content (with Page Number)
目錄	I
圖目錄	III
表目錄	IV
第一章  緒論	1
1-1  研究背景與動機	1
1-2  研究目的	4
1-3  研究架構	5
第二章  文獻回顧	6
2-1  旅遊需求預測	6
2-2  模糊時間序列	7
第三章  研究方法	13
3-1  S&C的模糊時間序列模型	13
3-2  適應性模糊時間序列模型(Adaptive fuzzy time series model)	17
3-2-1  適應性模糊時間序列模型--預測大學註冊人數	19
3-3 傅立葉級數(Fourier series method)修正殘差	25
3-3-1  傅立葉級數修正殘差—預測大學註冊人數	30
第四章  實證研究-預測日本來台旅遊人數	47
4-1  適應性模糊時間序列模型	47
4-2  傅立葉級數修正殘差	54
第五章  結論與建議	64
5-1 研究成果與結論	64
5-2 後續研究與建議	65
參考文獻	66
一、	中文部份	66
二、	英文部分	67

圖目錄
圖1-1 研究架構	5
圖3-1 實際註冊人數與適應性模糊時間序列結果比較	25
圖3-2 預測步驟	26
圖3-3 與其他模式預測註冊人數的結果作比較	35
圖3-4 實際註冊人數與預測註冊人數結果比較	39
圖3-5 實際註冊人數與預測註冊人數結果比較	45
圖4-1實際旅遊人數與預測結果	57
圖4-2 實際旅遊人數與預測結果	62

 
表目錄
表3-1 註冊人數模糊化結果	15
表3-2 語意值區間歷史資料個數	20
表3-3區間再次分割的結果	20
表3-4 實際註冊人數與模糊化註冊人數	21
表3-5波動型矩陣	22
表3-6 適應性模糊時間序列預測結果	24
表3-7 歷史實際註冊人數與模糊註冊人數	32
表3-8 第二階模糊邏輯關係組與預測值	32
表3-9 註冊人數的預測	33
表3-10 與其他模式預測註冊人數的結果作比較	34
表3-11 歷史實際資料和模糊註冊人數	37
表3-12 第二階模糊邏輯關係組	37
表3-13 預測註冊人數	38
表3-14 不同z下的預測結果	40
表3-15 歷史實際資料和模糊註冊人數	42
表3-16 第二階模糊邏輯關係組	42
表3-17 權重變化	43
表3-18 預測註冊人數	44
表3-19不同z下的預測結果	46
表4-1歷史資料落在區間個數	48
表4-2 再次分割區間	48
表4-3 實際旅遊人數與模糊化旅遊人數	50
表4-4 波動型矩陣	50
表4-5 適應性模糊時間序列預測結果	52
表4-6 模糊邏輯關係組與預測值	55
表4-7 權重變化	56
表4-8 旅遊人數的預測	56
表4-9 傳統方法與本方法預測結果之比較	57
表4-10 模糊邏輯關係組與預測值	59
表4-11 權重變化	60
表4-12 旅遊人數的預測	61
表4-13 不同z下的預測結果	63
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