關鍵字搜尋趨勢對預測日本、南韓來台旅客人數之影響-以時間序列、深度學習兩種方法

The Effect of Keyword Search Trend on Predicting Numbers of Japanese and Korean Tourists to Taiwan-Applying Time Series and Deep Learning Methods

楊士平(Shi-Ping Yang)

指導教授 ： 周清江

淡江大學/商管學院/資訊管理學系碩士班/碩士(2020年)

https://doi.org/10.6846/TKU.2020.00522

摘要

現今觀光旅遊業在台灣是主要經濟收入來源之一，受益於網路快速發展，使用電腦及手機查詢網路資料已經成為人們規劃旅遊不可或缺的一部份，我們認為Google搜尋引擎的查詢資料紀錄可以反應出搜尋者對旅遊的偏好趨勢。過去研究以傳統的時間序列SARIMA模型及機器學習來建置模型，本研究蒐集2011年至2018年之每月南韓及日本來台人數數據，加上同期的Google關鍵字趨勢資料，比較時間序列SARIMA模型及深度學習的長短期記憶模型之預測效果，探究深度學習在時間序列資料應用的成效。

關鍵字

旅客人數預測 ； 關鍵字搜尋趨勢 ； 時間序列 ； SARIMA模型 ； 深度學習 ； 長期短期記憶模型

並列摘要

Tourism is one of the main sources of GDP (Gross Domestic Product) in Taiwan. Web searching for internet data using computers and mobile phones is becoming more and more common. It has become indispensable for people to arrange tourism activities. We believe Google search engine data records could reflect internet user's preference for travel trends. In past researches, traditional time series SARIMA models and machine learning method have been used to build models. This study collects each month's numbers of Japanese and Korean tourists to Taiwan from 2011 to 2018, plus the corresponding Google keyword trend data in the same period. We compare the prediction effectiveness of the SARIMA model and the long short-term memory models of deep learning, to explore the effectiveness of deep learning in the application of time series data.

並列關鍵字

Tourists Numbers Predicting ； Keyword Search Trend ； Time Series ； SARIMA Model ； Deep Learning ； Long Short Term Memory

參考文獻

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