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姓名	廖心妤(Hsin-Yu Liao)  查詢紙本館藏	畢業系所	資訊管理學系
論文名稱	運用深度學習與機器學習於旅店取消訂單量預測模型
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檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2028-8-1以後開放)
摘要(中)	旅遊業是全球經濟中重要且快速發展的行業。近年來，旅遊業面臨著許多挑戰，直接影響到旅店業。特別是互聯網時代，線上訂房平台改變消費者行為，增加了取消訂單的頻率。為應對這些問題，旅店業需要準確的需求預測工具。本研究旨在建立旅店每日取消訂單量預測模型，並探索三種不同的方法在此領域的應用，包括分類模型、時間序列模型和模型混合。本研究使用個案公司提供的四間旅店真實資料集作為研究。在分類預測會使用機器學習算法，如Random Forest、XGBoost以及LGBM，將其應用在客戶訂單上；在時間序列預測則是使用簡單移動平均(SMA)、週期性移動平均(Periodic SMA)及LSTM Seq2Seq；模型混合方面是使用加權平均將分類和時間序列的預測結果做混合。結果顯示在四間旅店的分類模型實驗中，除客戶訂單特徵外，加入日期、時間相關特徵及特徵工程所創的新特徵能提高預測準確度。其次，時間序列模型實驗中使用單變量預測，深度學習模型(LSTM Seq2Seq)並不比傳統時間序列預測模型(Periodic SMA)準確。使用Periodic SMA於四間旅店的WMAPE平均下降約46%。此外，使用LSTM Seq2Seq，其多變量預測結果優於單變量，四間旅店的WMAPE平均下降約36%。其次，模型混合的結果比分類轉時序或時間序列模型更佳，四間旅店的WMAPE平均下降約3.3%。最後，四間旅店的數據聯合訓練並考慮旅店特徵，可進一步提高預測準確性，四間旅店的AUC平均提升約1.6%。由本研究的分析可知模型混合有助於為旅店業提供全面且有效的旅店取消訂單量預測解決方案。
摘要(英)	Tourism is an important and fast-growing industry in the global economy. In recent years, the tourism industry has faced many challenges that directly affect the hotel industry. Especially in the Internet age, online room booking platforms have changed consumer behavior and increased the frequency of cancellations. To deal with these problems, the hotel industry needs accurate demand forecasting tools. The purpose of this study is to establish a forecasting model for daily cancellation orders in hotels, and to explore the application of three different methods in this field, including classification models, time series models, and model hybriding.This study uses the real data sets of four hotels provided by the case company as research. Machine learning algorithms, such as Random Forest, XGBoost, and LGBM, are used in classification forecasting and applied to customer orders; in time series forecasting, simple moving average (SMA), periodic moving average (Periodic SMA) and LSTM Seq2Seq are used ; In terms of model hybriding, the weighted average is used to hybrid the prediction results of classification and time series.The results show that: In the classification model experiments of the four hotels, in addition to customer order features, adding date and time-related features and new features created by feature engineering can improve the prediction accuracy; In the time series model experiments, single For variable prediction, the deep learning model (LSTM Seq2Seq) is not more accurate than the traditional time series (Periodic SMA). The WMAPE of the four hotels using Periodic SMA dropped by about 46% on average. In addition, using LSTM Seq2Seq, its multivariate prediction results are better than univariate, and the average WMAPE of the four hotels drops by about 36%. A decrease of about 3.3%;The joint training of the data of the four hotels and considering the characteristics of the hotels can further improve the prediction accuracy, and the AUC of the four hotels has increased by about 1.6% on average. The analysis of this study shows that model hybriding helps to provide a comprehensive and effective hotel cancellation order forecasting solution for the hotel industry.
關鍵字(中)	★ 旅店取消訂單 ★ 機器學習 ★ 深度學習 ★ 模型混合	關鍵字(英)	★ hotel cancellation prediction ★ machine learning ★ deep learning ★ model hybriding
論文目次	摘要 i ABSTRACT ii 致謝辭 iii 目錄 iv 圖目錄 vii 表目錄 viii 第一章、緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究目的與問題 4 第二章、文獻回顧與探討 5 2.1 旅店需求預測 5 2.2 其他領域的需求預測 12 第三章、 研究方法 15 3.1 資料來源與前處理 15 3.2 分類模型建構 16 3.2.1變數定義 16 3.2.2分類演算法 18 3.3 時間序列模型 19 3.3.1變數定義 19 3.3.2時間序列演算法 20 3.4 模型混合 22 第四章、實驗評估 24 4.1 資料處理與後續分析流程 24 4.1.1 訓練集與測試集的切分 25 4.1.2 訓練集與驗證集的切分 25 4.1.3 貝葉斯優化 27 4.2 實驗設計與問題 29 4.2.1 實驗 1 29 4.2.2 實驗 2 30 4.2.3 實驗 3 30 4.3 評估指標 31 4.3.1分類 31 4.3.2 迴歸 32 4.4 實驗結果 34 4.4.1 實驗 1 34 4.4.2 實驗 2 40 4.4.2.1 分類轉時間序列 40 4.4.2.2 時間序列 46 4.4.2.3 模型混合 48 4.4.3 實驗 3 51 4.5 討論 52 4.5.1 實驗 1 52 4.5.2 實驗 2 53 4.5.2.1 分類轉時間序列 53 4.5.2.2 時間序列 55 4.5.2.3 模型混合 56 4.5.3 實驗 3 56 4.5.4 分類模型中的特徵統計檢定 57 第五章、研究結論與建議 67 5.1 研究結論 67 5.2 研究限制與未來研究方向 68 參考文獻 70 附錄一 76
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指導教授	胡雅涵(Ya-Han Hu)	審核日期	2023-6-30
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