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研究生:劉珊如
研究生(外文):Shan-Ru Liu
論文名稱:最小優先方法於動態時間校正的分析與應用
論文名稱(外文):Analysis and Applications of the Minimum First Method for Dynamic Time Warping
指導教授:楊昌彪楊昌彪引用關係
指導教授(外文):Chang-Biau Yang
學位類別:碩士
校院名稱:國立中山大學
系所名稱:資訊工程學系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:108
語文別:英文
論文頁數:159
中文關鍵詞:最小優先方法量化指標時間序列分類問題動態時間校正
外文關鍵詞:quantitative indicatorminimum first methoddynamic time warpingclassification problemtime series
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時間序列是日常生活中很常用的資料,例如股票指數、匯率、心電圖等等。給定一條時間序列,時間序列分類問題是在一堆已分類好的類別中指派一個類別給它,解決時間序列分類問題的核心是計算兩條時間序列的距離。計算距離最常使用的方法之一是動態時間校正 (DTW),它的時間為O(n^2),n代表給定的時間序列的長度,O(n^2)時間的執行效率不夠好,特別是對於長時間序列。為了減少DTW的執行時間,Chen等人在2018年提出了最小優先方法 (MFM) 來加快DTW的計算速度,並仍然維持它原來的準確度。雖然MFM在大多數的情況下都很有效率,但在某些資料集中,MFM比原始的DTW更差,換句話說,MFM適用於某些資料集,而原始的DTW方法適用於其他資料集。因此,在本論文中,我們訓練了兩個量化指標,包含變化量的標準差和波的振盪來自動判斷哪些資料集適合使用MFM,而這種適應性預測的準確度非常高。除此之外,我們還將MFM應用至其他DTW的相關方法,包含有限制範圍的DTW和用於稀疏時間序列的AWarp。
The time series information is commonly used in daily life, such as stock indices, exchange rates, and electrocardiograms. Given a target time series, the time series classification (TSC) problem is to assign it a class in a set of classified classes. The essential step for solving the TSC problem is the distance calculation of two time series. One of the most common ways for calculating the distance is dynamic time warping (DTW), with O(n^2) time, where n denotes the length of the given time series. The execution efficiency of O(n^2) time is not good enough, especially for long time series. In order to practically reduce the execution time of DTW, Chen et al. proposed the minimum first method (MFM) in 2018 to speed up the calculation of DTW and to still maintain its original classification accuracy. MFM is effective in most cases, but MFM may be worse than the original DTW in some other datasets. In other words, MFM is suitable for some datasets and the original DTW method is suitable for other datasets. This thesis trains two quantitative indicators, including standard deviation of variations and wave oscillation, to automatically determine which datasets are suitable for using MFM. The accuracy of such suitability prediction is very high. Furthermore, we also apply MFM to other DTW related methods, including DTW with band constraints and AWarp: warping distance for sparse time series.
THESIS VERIFICATION FORM . . . . . . . . . . . . . . . . . . . . . . i
THESIS AUTHORIZATION FORM . . . . . . . . . . . . . . . . . . . . iii
ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
CHINESE ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi
LIST OF SYMBOLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxiv
Chapter 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Chapter 2. Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1 The Time Series Classi cation Problem . . . . . . . . . . . . . . . . . 4
2.2 The Euclidean Distance . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.3 Dynamic Time Warping . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.4 Dynamic Time Warping with Band Constraints . . . . . . . . . . . . 7
2.4.1 The Sakoe-Chiba Band . . . . . . . . . . . . . . . . . . . . . . 7
2.4.2 The Itakura Parallelogram Band . . . . . . . . . . . . . . . . 7
2.5 AWarp: Warping Distance for Sparse Time Series . . . . . . . . . . . 9
2.6 The Minimum First Method for Dynamic Time Warping . . . . . . . 10
2.7 UCR Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Chapter 3. Our Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.1 Analysis of the Minimum First Method . . . . . . . . . . . . . . . . . 14
3.1.1 Observations of Time Series . . . . . . . . . . . . . . . . . . . 15
3.1.2 The Standard Deviation of the Variation Sequence . . . . . . 19
3.1.3 The Oscillation Indicator . . . . . . . . . . . . . . . . . . . . . 19
3.1.4 Combined Indicators . . . . . . . . . . . . . . . . . . . . . . . 20
3.2 Applications of the Minimum First Method . . . . . . . . . . . . . . . 21
3.2.1 The Sakoe-Chiba Band with the Minimum First Method . . . 24
3.2.2 The Itakura Parallelogram Band with the Minimum First Method 27
3.2.3 AWarp with the Minimum First Method . . . . . . . . . . . . 28
Chapter 4. Experimental Results . . . . . . . . . . . . . . . . . . . . . . 34
4.1 Sparse Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.2 Experimental Procedures . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2.1 The Procedure of UCR Datasets . . . . . . . . . . . . . . . . . 35
4.2.2 The Procedure of Sparse Datasets . . . . . . . . . . . . . . . . 36
4.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.3.1 Results of UCR Datasets . . . . . . . . . . . . . . . . . . . . . 37
4.3.2 Results of Sparse Datasets . . . . . . . . . . . . . . . . . . . . 48
Chapter 5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
Appendixes
A. UCR Datasets Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
A.1 Original UCR Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . 59
A.2 Var Std and Oscillations for Sampled UCR Datasets . . . . . . . . . . 62
A.3 Classi cation Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . 65
A.4 Time and Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
A.4.1 DTW & DTW-M . . . . . . . . . . . . . . . . . . . . . . . . . 68
A.4.2 DTW- & DTW-M . . . . . . . . . . . . . . . . . . . . . . . 73
A.4.3 SCBand & SCBand-M . . . . . . . . . . . . . . . . . . . . . . 78
A.4.4 SCBand- & SCBand-M . . . . . . . . . . . . . . . . . . . . 83
A.4.5 ITABand & ITABand-M . . . . . . . . . . . . . . . . . . . . . 88
A.4.6 ITABand- & ITABand-M . . . . . . . . . . . . . . . . . . . 93
A.5 Training Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
A.6 Average and Standard Deviation of Ten Execution Times . . . . . . . 102
B. Sparse Datasets Results . . . . . . . . . . . . . . . . . . . . . . . . . . 129
B.1 Length 100 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
B.2 Length 200 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
B.3 Length 500 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
B.4 Length 1000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
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