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研究生: 楊軒
Yang, Hsuan
論文名稱: 基於時間序列、迴歸和正規化的快速預測PM2.5方法
A fast PM2.5 forecast approach based on time series data analysis, regression and regularization
指導教授: 陳伶志
Chen, Ling-Jyh
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2018
畢業學年度: 106
語文別: 中文
論文頁數: 57
中文關鍵詞: PM2.5空氣汙染線性迴歸正規化時間序列預測
英文關鍵詞: PM2.5, Air Pollution, Linear Regression, Regularization, Time series, Forecast
DOI URL: http://doi.org/10.6345/THE.NTNU.DCSIE.032.2018.B02
論文種類: 學術論文
相關次數: 點閱:197下載:43
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  • 隨著城市化和全球化的不斷推進,空氣污染已經成為一個全球性的問題。儘管研究人員一直在試圖找到解決空氣污染的辦法,但空氣汙染中還是有許多問題無法解決,因此透過資料科學進行預測達到預防空氣污染變成了自我保護的重要關鍵。
    本研究透過了線性迴歸、正規化、時間序列以及布建在臺灣地區大量的PM2.5感測器預測未來五小時內的PM2.5數值,不同於其他多數預測方法參考了許多參數,我們只參考了PM2.5歷史資料這單一資料。透過上述的觀念我們設計了一項適定性迭代法 (Adaptive Iterative Forecast) 進行預測,能夠根據歷史資料的變化,快速預測出未來數小時PM2.5的數值。
    整體研究致力的方向為更快速地建立出準確的預測模型,經由各種比較分析在最後的實驗結果中我們證實已達到了上述成果。我們也將整體研究成果建置成一套預測系統廣泛應用於全臺各地,讓使用者能透過預測出的結果進行個人空氣品質的防護作用。

    Air pollution has become a global problem because of the continuous urbanization and globalization. Although, researchers have been trying to come up with solutions to tackle air pollution but still there are many loopholes that need be addressed to have an effective air pollution monitoring system. One way to tackle this problem by using - data science.
    In this research, we use linear regression and regularization, to forecast the PM2.5 values for the next five hours using PM2.5 data obtained from large scale PM2.5 sensors deployment in Taiwan. Our method is a data centric method and we use historical PM2.5 data to do the forecast. In our work , we designed an Adaptive Iterative Forecast (AIF) method for forecasting, AIF can rapidly forecast the PM2.5 based on the changes in historical data.
    The goal of the research is to develop efficient and accurate forecast models. Through various comparative analyses, we have proved that our model can achieve significant results. Based on the results, we have also built a forecasting system which is widely used throughout Taiwan. Such a system allows the users to stay aware of the air quality and plan their day to day life.

    摘要 ii Abstract iii 致謝 iv 附圖目錄 vii 第一章 介紹 1 第二章 相關文獻探討 4 2.1 時間序列預測模型 (Time series model) 4 2.2 線性迴歸 (Linear Regression) 6 2.3 機器學習 (Machine Learning) 8 2.4 神經網路 (Neural Network) 10 第三章 研究方法 16 3.1 資料預處理 17 3.2 預測模型 19 3.3 預測方法 24 3.3.1 重複式預測法 (Repeating forecast) 24 3.3.2 跳躍式預測法 (Jumping forecast) 25 3.3.3 迭代式預測法 (Iterating forecast) 25 3.4 固定性決定參數以及適定性決定參數 26 3.4.1 適定性決定參數 λ 27 3.4.2 適定性決定參數 h 28 第四章 實驗與結果 29 4.1 實驗資料 29 4.2 固定性參數以及適定性參數比較結果 30 4.3 與其他時間序列預測以及迴歸預測介紹與比較 37 4.3.1 天真預測法 (Naive method, NM) 37 4.3.2 加權移動平均法 (Weight moving average, WMA) 37 4.3.3 多元線性迴歸預測 (Multiple linear regression, MLR) 38 4.3.4 多元適應性迴歸預測(Multivariate adaptive regression spline, MARS) 38 4.4 類神經網路和機器學習類預測法正確率比較 45 4.4.1 Xgboost 演算法 45 4.4.2 SVM 演算法 46 4.4.3 Hybrid 預測法 (神經網路搭配ARIMA時間序列) 46 第五章 應用系統架構 49 第六章 結論與未來展望 53 參考文獻 54

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