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研究生: 蘇俊瑋
Su, Chun-Wei
論文名稱: 應用統計學習於網路聲量及空氣品質之研究---以台南地區為例
Application of Statistical Learning to Internet Opinions and Air Quality --- Taking Tainan Area as an Example
指導教授: 李坤洲
Lee, Kun-Chou
學位類別: 碩士
Master
系所名稱: 工學院 - 系統及船舶機電工程學系
Department of Systems and Naval Mechatronic Engineering
論文出版年: 2024
畢業學年度: 112
語文別: 中文
論文頁數: 98
中文關鍵詞: 統計學習Google Trends網路聲量空氣品質指標動態時間扭曲
外文關鍵詞: Statistical Learning, Google Trends, Internet Opinions, Air Quality index, Dynamic Time Warping
ORCID: 0009-0003-4846-8497
相關次數: 點閱:63下載:0
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  • 摘要 II Extended Abstract III 誌謝 VII 目錄 VIII 圖目錄 X 表目錄 XII 第一章 緒論 1 §1.1 研究動機與目的 1 §1.2 文獻回顧 1 §1.3 研究架構 4 第二章 關鍵字及空氣品質數據蒐集 6 §2.1 Google Trends計算指標 6 §2.2 關鍵字選擇分析 8 §2.2.1 境外污染與季風效應 8 §2.2.2 關鍵詞選擇及分類 9 §2.3 空氣品質數據蒐集 11 §2.3.1 空氣品質指標(Air Quality Index, AQI) 11 §2.3.2 資料來源 11 §2.3.3 資料篩選 12 §2.4 使用 Pytrends 取得搜尋趨勢數據 13 §2.4.1 網路聲量數據來源 13 §2.4.2 網路聲量數據蒐集方法 13 第三章 統計學習之模型建置 33 §3.1 多元線性回歸模型 33 §3.2 泊松回歸模型 33 §3.3 深度神經網路模型 34 §3.3.1 卷積神經網路概念與架構 34 §3.3.2 卷積層(Convolutional Layer) 34 §3.3.3 池化層(Pooling Layer) 35 §3.3.4 平化層(Flatten Layer) 35 §3.3.5 隨機關閉神經元(Dropout Layer) 36 §3.4 動態時間扭曲(Dynamic Time Warping)特徵 37 §3.4.1 動態時間扭曲 37 §3.4.2 成本矩陣圖 38 §3.4.3 DTW特徵計算 39 §3.4.4 DTW特徵用於模型 39 §3.5 模型評估指標 40 §3.5.1 MSE(Mean-Square Error)均方誤差 40 §3.5.2 RMSE(Root Mean Squared Error)均方根誤差 41 §3.5.3 MAE(Mean Absolute Error)平均絕對誤差 41 §3.5.4 MAPE(Mean Absolute Percentage Error)平均絕對百分比誤差 41 §3.5.5 DIST(Dynamic Time Warping Distance)動態時間扭曲距離 42 第四章 時間序列之統計學習預測分析 52 §4.1 模型實驗過程 52 §4.1.1 線性回歸模型主程式 52 §4.1.2 泊松回歸模型主程式 52 §4.1.3 深度神經網路模型主程式 53 §4.2 結合關鍵詞與DTW特徵進行模型效能評估 54 第五章 結論與未來展望 81 §5.1 結論 81 §5.2 未來展望 82 參考文獻 83

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