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    Title: 運用Google大數據預測觀光客之流入數量-以台灣為例
    Forecasting Tourist inflow using Google Trends data The case of Taiwan
    Authors: 謝薇安
    Saenz A, Vivian
    Contributors: 林左裕
    Lin, Tsoyu Calvin
    謝薇安
    Saenz A, Vivian
    Keywords: 預測
    VAR
    Google預測
    台灣
    消費者行為
    機器學習
    Forecasting
    VAR
    Google
    Trend
    Taiwan
    Consumer Behavior
    Date: 2018
    Issue Date: 2018-07-17 11:35:27 (UTC+8)
    Abstract: Google Trend is an Online tool that provides information to daily and weekly data on the frequency of certain search keywords, objects, and phrases in a given time period. A number of studies showed that these data can be used to ‘'Nowcasting''and‘'Google Forecasting Econometrics'', it can be concluded that data on the Internet search can be used in predictive purposes in the wider area of economic activity. As many studies have already pointed out the value of data on the Internet searching for the purpose of predictions of tourist demand in the wider and narrower levels destinations, but also on businesses levels.

    The aim of this paper is to examine whether Google trend could be used to predict tourist arrival in Taiwan. For this study, January 2008 through December 2017 was used as the analyzed period. This study tries to build a forecasting model of visitors to Taiwan by incorporating Google Trend
    Search Keywords and statistics data from the Taiwan Tourism Bureau. We forecast 5 years in this paper 2018,2019, 2020, 2021,2022.
    Using forecasting performance of various vector autoregressive (VAR) models we found out that by incorporating the Google Trend Data Search,into autoregressive models improve the predictive ability of the model.

    The value of this study is the important value of the Big data nowadays and a better and costly way to analyze the consumer behavior.
    Google Trend is an Online tool that provides information to daily and weekly data on the frequency of certain search keywords, objects, and phrases in a given time period. A number of studies showed that these data can be used to ‘'Nowcasting''and‘'Google Forecasting Econometrics'', it can be concluded that data on the Internet search can be used in predictive purposes in the wider area of economic activity. As many studies have already pointed out the value of data on the Internet searching for the purpose of predictions of tourist demand in the wider and narrower levels destinations, but also on businesses levels.

    The aim of this paper is to examine whether Google trend could be used to predict tourist arrival in Taiwan. For this study, January 2008 through December 2017 was used as the analyzed period. This study tries to build a forecasting model of visitors to Taiwan by incorporating Google Trend
    Search Keywords and statistics data from the Taiwan Tourism Bureau. We forecast 5 years in this paper 2018,2019, 2020, 2021,2022.
    Using forecasting performance of various vector autoregressive (VAR) models we found out that by incorporating the Google Trend Data Search,into autoregressive models improve the predictive ability of the model.

    The value of this study is the important value of the Big data nowadays and a better and costly way to analyze the consumer behavior.
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    Description: 碩士
    國立政治大學
    應用經濟與社會發展英語碩士學位學程(IMES)
    1052660081
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G1052660081
    Data Type: thesis
    DOI: 10.6814/THE.NCCU.IMES.003.2018.F06
    Appears in Collections:[應用經濟與社會發展英語碩士學位學程 (IMES) ] 學位論文

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