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研究生:程明潔
研究生(外文):CHENG,MING-CHIEH
論文名稱:預測分析東協五國來台旅遊人數
論文名稱(外文):Forecasting Tourist Arrivals from ASEAN 5 to Taiwan
指導教授:陳婉淑
指導教授(外文):Chen, Woan-Shu
口試委員:蔡恆修許英麟
口試委員(外文):Tsai, Heng,hsiuHsu, Ying-Lin
口試日期:2017-06-07
學位類別:碩士
校院名稱:逢甲大學
系所名稱:應用統計研究所
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:45
中文關鍵詞:整合移動平均自迴歸模型 (ARIMA)自下而上的方法季節指數平滑方法階層式預測平均絕對百分比誤差 (MAPE)最佳組合方法樣本外預測自上而下的方法不可觀測組件模型 (UCM)
外文關鍵詞:ARIMABottom-up methodExponential smoothingHierarchical forecastingMean absolute percentage error (MAPE)Optimal combination methodOut-of-sample forecastTop- down methodUnobserved Components Model (UCM)
相關次數:
  • 被引用被引用:5
  • 點閱點閱:481
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  • 下載下載:51
  • 收藏至我的研究室書目清單書目收藏:0
本研究利用八種預測模型和方法對東協到台灣的遊客人次的前五名國家(ASEAN 5)配適時間序列預測模型和評估預測方法之表現。本研究採取樣本外 預測,我們保留最後十二筆資料作為檢查樣本外的預測模型之表現。我們使用滾動式移動窗口作一步到十二步預測,共產生了六次滾動式移動窗口的結果,以評估樣本外的準確性。我們考慮八種預測模型 (或方法),其中階層式時間序列預測主要的三種方法:(1)自上而下的方法,(2)自下而上的方法,(3)最佳組合方法; 以及 (4) 五個國家分別配適 ARIMA 模型之合計; (5)季節指數平滑方法; (6)時間序列迴歸模型; (7)ARIMA 模型; (8)和 UCM 模型(Unobserved components model)。我們使用四種預測準則來評估八種模型(或方法) 的表現能力,分別為均方根誤差 (RMSE)、平均絕對偏差 (MAD)、平均百分比誤差 (MPE) 和平均絕對百分比誤差 (MAPE) 這四種準則。本文的目的是比較八種模型 (或方法) 的預測能力,我們感興趣的是究竟哪一個預測方法能在我們的研究案例中脫穎而出。預測結果顯示,由於在 2015 年 8 月“新南向政策”實施後,可看出從泰國來台旅客的預測預測值在 8 月後皆低於真實值,表示該項政策的開放確實增加了來台旅遊的人次,有助於推動台灣旅遊市場。本文也採用排名法評估四種預測準則和五次滾動式移動結果,結果為季節指數平滑方法優於其他七種預測模型或方法。


This study evaluates the Taiwan government’s initiative “New Southbound Policy” based on the performances of eight prediction methods for the country’s inbound tourism arrivals from ASEAN 5. We examine out-of-sample forecast accuracy by using a holdout set and consider three main types of a hierarchical time series forecasting approach: (1) bottom-up approach,(2) top-down approach, and (3) optimal combination approach; we then employ an (4) aggregation of forecasts from each ARIMA model; (5) Holt-Winters exponential smoothing method for aggregated tourism arrivals from ASEAN 5; (6) a time series regression for aggregated tourism of ASEAN 5; (7) the ARIMA model for aggregated tourism arrivals from ASEAN 5; and (8) the unobserved components model for aggregated tourism arrivals from ASEAN 5. We evaluate how well the eight models/methods can forecast by computing four criteria: the root mean square error (RMSE), the mean absolute deviation (MAD), the mean percent error (MPE), and the mean absolute percentage error (MAPE). We measure the forecast accuracy of these eight forecasting models/methods with four criteria by a rolling window approach. Based on the forecasts’ performances, we conclude that the “New Southbound Policy” does help boost Taiwan’s tourism market.
Moreover, the multiplicative Holt-Winters method is at the top based on a ranking system, while the unobserved components model exhibits the second-best rank.
1 Introduction 1

2 Methodology 5

3 Measuring forecast accuracy 14

4 Data analytics 15

5 Conclusion and discussion 28
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