臺灣博碩士論文加值系統

本論文發展一以決策樹為基礎的氣溫與雨量之預測模型。由於傳統決策樹都是以區間化輸出為主，因此該預測模型將線性複迴歸模型整合至決策樹以達成數值化輸出。本論文利用該預測模型來預測領前1至7天的氣溫與雨量，並針對預測值給予一定信賴水準的信賴區間。為了說明此預測模型的效能，該預測模型與其他時間序列的預測方法進行比較，其中時間序列的預測方法包括自迴歸、移動平均法、自迴歸差分整合移動平均法。

Based on decision tree, the purpose of this thesis is to develop a forecast model for temperature and rainfall. Because the traditional decision tree generates interval output, the forecast model integrates the multiple linear regression model into the decision tree in order to achieve the goal of numeric output. In this thesis, the seven days ahead temperature and rainfall are predicted by using the forecast model, and their confidence intervals are given at a confidence level. In order to demonstrate the effectiveness of the forecast model, we compare the forecast model with some different time series methods, such as autoregressive (AR), moving average(MA), autoregressive integrated moving average (ARIMA).

摘 要 i
ABSTRACT ii
誌 謝 iii
目 錄 iiv
圖 目 錄 vii
表 目 錄 x
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究方法 2
1.3 研究目的 3
1.4 各章節簡述 5
第二章 文獻探討與回顧 6
2.1 天氣預測 6
2.2 資料探勘 5
2.3 常見的預測方法 7
2.3.1 自迴歸模型 7
2.3.2 移動平均模型 8
2.3.3 自迴歸差分整合移動平均模型 8
2.3.4 決策樹 9
2.4 決策樹 10
2.4.1 疊代二元樹3代 10
2.4.2 C4.5 10
2.4.3 CART 11
2.4.4 模糊決策樹 12
2.5 複迴歸模型 13
第三章 決策樹與複迴歸預測方法 15
3.1 資料處理 15
3.1.1 天氣資料集 15
3.1.2 資料前處理 17
3.2 決策樹分析 20
3.3 複迴歸分析 21
3.4 評估 21
3.4.1 平均絕對誤差 21
3.4.2 均方根誤差 21
3.4.3 平均絕對百分比誤差 21
3.4.4 皮爾森相關係數 22
3.4.5 信賴區間 23
第四章 實驗與討論 27
4.1 資料的相關性分析 27
4.1.1 氣溫的相關性分析 27
4.1.2 雨量的相關性分析 27
4.2 建立決策樹 28
4.2.1 氣溫的決策樹建立 28
4.2.2 雨量的決策樹建立 29
4.3 複迴歸分析 31
4.3.1 氣溫複迴歸分析 31
4.3.2 雨量複迴歸分析 33
4.4 評估預測好壞 37
4.4.1 氣溫迴歸決策樹預測 37
4.4.2 雨量迴歸決策樹預測 54
第五章 結論與未來展望 70
5.1結論 70
5.2未來展望 70
參考文獻 71

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