臺灣博碩士論文加值系統

多元時間序列(Multivariate time series,MTS)資料十分常見於現實生活中，由於大多數問題不只考慮單一變量，而是多個變量影響一個類別標籤(Label)，因此如何有效地解決多元時間序列分類問題成為本次研究方向。近年來隨著人工智慧(Artificial Intelligence,AI)快速的發展，嘗試深度學習框架處理多元時間序列分類問題。
本研究提出一解決多元時間序列分類問題之方法，藉由時間序列擁有趨勢之特性，將多元時間序列資料中，每一個變數所包含的時間序列利用迴歸分析找出其迴歸方程式，並利用迴歸係數與截距項進行分群，使得擁有相似趨勢之時間序列被分在相同一群，並根據過往文獻所發表的四種時間序列轉換圖像之方法，根據分群結果，將具有相似趨勢的時間序列，使用同一種時間序列轉換為圖像方法進行轉換，並實驗多種時間序列轉換圖像方法組合，決定每一群時間序列的轉圖方法。依照上述方法將多元時間序資料列轉換為圖像後，將輸入每一筆資料到3D卷積神經網路進行特徵擷取與圖像辨識，能有效解決多元時間序列分類問題，找出最佳的分類準確度。

Multivariate time series data is very common in real life. Since most problems not only consider a single variable, but also multiple variables affect the label, how to effectively solve the problem of multivariate time series classification remain a major problem in research. In recent years, with the rapid development of Artificial Intelligence (AI), the deep learning framework has been tried to deal with multivariate time series classification problems.
This study proposes a method to solve the problem of MTS classification. The multivariate time series data is used to find the regression equation by regression analysis. We use the regression coefficient and intercept to the cluster so that the time series with similar trends are divided into the same cluster, and the literature proposes to the four frameworks to encode time series data as different types of images. According to the clustering results, the time series with similar trends will be used the same method to encode time series into images and try a variety of experiment to determine encoding method for each cluster of time series. After encoding multivariate time series data as images according to the above method, each data is input into the 3D convolutional neural networks for feature extraction and image recognition, which can effectively solve the multivariate time series classification problem and find the best classification accuracy.

摘要 Ⅰ
Abstract Ⅱ
目錄 Ⅲ
圖目錄 Ⅳ
表目錄 Ⅴ
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 2
1.3 研究議題 3
1.3.1 如何決定時間序列相似趨勢 3
1.3.2 如何選擇相似趨勢之時間序列的轉圖方法 3
1.4 重要性 4
第二章 文獻探討 5
2.1 多元時間序列 5
2.2 迴歸分析 6
2.3 K-平均演算法 6
2.4 時間序列轉圖像 7
2.5 深度學習 8
2.6 卷積神經網路(convolutional neural networks,CNN)介紹 9
2.7 3D卷積神經網路(3D convolutional neural networks,3D CNN) 11
第三章 研究步驟與方法 13
3.1 研究流程與架構 13
3.2 資料預處理 16
3.2.1 資料整理 16
3.3 時間序列相似性 17
3.3.1 迴歸分析 17
3.3.2 時間序列分群 19
3.4 時間序列圖像化 22
3.4.1 Gramian matrix 22
3.4.2 Markov Transition Field 26
3.4.3 Recurrence plot 27
3.5 時間序列圖像化實驗組合 29
3.6 3D卷積神經網路 31
3.6.1 3D卷積神經網路架構 32
3.6.2 3D卷積神經網路流程 35
第四章 實作成果 39
4.1 資料介紹 39
4.1.1 多元時間序列折線圖 40
4.2 資料預處理 41
4.2.1 資料整理 41
4.3 實驗數據結果與分析 42
4.3.1 時間序列迴歸分析 42
4.3.2 多元時間序列分群 44
4.4 時間序列圖像化實驗組合 45
4.5 3D卷積神經網路 46
4.5.1 多元時間序列實驗分析 46
4.6 結果分析 54
第五章 結論與建議 57
5.1 結論 57
5.2 研究限制與未來建議 58
參考文獻 59

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