簡易檢索 / 詳目顯示

回結果列表
研究生： 楊晨宜
Chen-Yi YANG
論文名稱： 多維時間序列轉換應用於卷積神經網路
Multivariate Time Series Data Transformation for Convolutional Neural Network
指導教授： 楊朝龍
Chao-Lung Yang
口試委員: 花凱龍
Kai-Lung Hua
歐陽超
Chao Ou-Yang
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2018
畢業學年度： 106
語文別： 英文
論文頁數： 47
中文關鍵詞： 多變量時間序列分析 、時間序列轉二維圖像 、卷積神經網路
外文關鍵詞： Multivariate time series, Convolutional neural network, Time series data image encoding
相關次數： 點閱：362  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 本研究提出一個資料處理架構，將時間序列資料轉換為二維圖像，再整合多 變量的時間資料成為一張多維度的圖像。透過卷積神經網路法處理圖像資料的優 勢，以卷積神經網路找出其圖像的特徵，並依造其特徵預測狀態。本研究以四個 常見的圖像轉換方法：分別為Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), Markov Transition Field (MTF)和Recurrence plot (RP)，進行時間序列資料轉換圖像資料的工具。本研究比較多變量疊加法及多變 量延展法兩種資料整合的方式，並以開源資料進行方法的驗證與評估。研究結果 發現，透過時間序列資料轉換二維圖像並延展成多維度圖像，將有助於提升資料 預測的準確度。

    This thesis proposes a novel framework to encode time series data into two-dimensional images, and aggregate the images into one image for each batch of data. After transformation and aggregation, the images passed through a convolutional neural network, which is outstanding in dealing with computer vision problems. The convolutional neural network learned the image features and predict the status. This study applied four methods to encode time series into images: Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP). The overlaying and appending methods to aggregate the images were validated and evaluated using open datasets. The results of the experiments find that encoding time series data into images and aggregating the images by the appending method are helpful in increasing prediction accuracy.

    CONTENTS 摘要........................................................................................................................ I ABSTRACT ......................................................................................................... II CONTENTS.......................................................................................................... 1 LIST OF FIGURES ............................................................................................. 3 LIST OF TABLES ............................................................................................... 5 CHAPTER 1. INTRODUCTION .................................................................... 6 1.1. Background ................................................................................................. 6 1.2. Difficulties and Challenges ......................................................................... 6 1.3. Research Problem ....................................................................................... 7 1.4. Thesis Structure .......................................................................................... 7 CHAPTER 2. LITERATURE REVIEW ........................................................ 9 2.1. Image Time Series Data .............................................................................. 9 2.2. Multivariate Time Series Classification .................................................... 11 2.3. Convolutional Neural Network ................................................................. 12 2.3.1. Convolutional Layer ........................................................................ 13 2.3.2. Activation Function ......................................................................... 13 2.3.3. Pooling Layer .................................................................................. 14 2.3.4. Fully-connected Layers ................................................................... 15 2.3.5. Class Activation Mapping ............................................................... 16 CHAPTER 3. METHODOLOGY ................................................................. 17 3.1. Data ........................................................................................................... 18 3.2. Data Preprocessing .................................................................................... 18 3.3. Data Encoding as Images .......................................................................... 19 3.3.1. Gramian Angular Field.................................................................... 20 3.3.2. Markov Transition Field.................................................................. 21 3.3.3. Recurrence Plot ............................................................................... 22 3.4. Image Aggregation .................................................................................... 23 3.4.1. Overlaying Method ......................................................................... 24 3.4.2. Appending Method .......................................................................... 25 2 3.4.3. Comparison of the Overlaying Method and the Appending Method25 3.5. The Structure of a Convolutional Neural Network ................................... 26 3.5.1. The Parameters of CNN .................................................................. 26 3.6. Experiments .............................................................................................. 27 3.7. Performance Evaluation ............................................................................ 27 CHAPTER 4. EXPERIMENTS AND RESULTS ........................................ 28 4.1. Results of the Overlaying Method and the Appending Method................ 28 4.2. The Parameter of the Image Resolution .................................................... 32 4.3. Different Orders in the Appending Method .............................................. 35 4.4. Important Part of the Input Image ............................................................. 37 CHAPTER 5. CONCLUSION ....................................................................... 38 REFERENCES ................................................................................................... 40

    Anderson, Charles W, Erik A Stolz, and Sanyogita Shamsunder. 1998. 'Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks', IEEE Transactions on Biomedical Engineering, 45: 277-86. Batal, Iyad, Lucia Sacchi, Riccardo Bellazzi, and Milos Hauskrecht. 2009. "Multivariate Time Series Classification with Temporal Abstractions." In FLAIRS Conference. Berndt, Donald J, and James Clifford. 1994. "Using dynamic time warping to find patterns in time series." In KDD workshop, 359-70. Seattle, WA. Box, George EP, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung. 2015. Time series analysis: forecasting and control (John Wiley & Sons). Chen, Jou-Fan, Wei-Lun Chen, Chun-Ping Huang, Szu-Hao Huang, and An-Pin Chen. 2016. "Financial Time-Series Data Analysis Using Deep Convolutional Neural Networks." In Cloud Computing and Big Data (CCBD), 2016 7th International Conference on, 87-92. IEEE. Chen, Yanping and Keogh, Eamonn and Hu, Bing and Begum, Nurjahan and Bagnall, Anthony and Mueen, Abdullah and Batista, Gustavo. 2015. 'The UCR Time Series Classification Archive'. Donahue, Jeff, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell. 2014. "Decaf: A deep convolutional activation feature for generic visual recognition." In International conference on machine learning, 647-55. Eckmann, J-P, S Oliffson Kamphorst, and David Ruelle. 1987. 'Recurrence plots of dynamical systems', EPL (Europhysics Letters), 4: 973. Fan, Jialue, Wei Xu, Ying Wu, and Yihong Gong. 2010. 'Human tracking using convolutional neural networks', IEEE Transactions on Neural Networks, 21: 1610-23. Faouzi, Johann. 2018. 'pyts: a Python package for time series transformation and classification'. Fox, Emily B, Michael C Hughes, Erik B Sudderth, and Michael I Jordan. 2014. 'Joint modeling of multiple time series via the beta process with application to motion capture segmentation', The Annals of Applied Statistics: 1281-313. Górecki, Tomasz, and Maciej Łuczak. 2015. 'Multivariate time series classification with parametric derivative dynamic time warping', Expert Systems with Applications, 42: 2305-12.
    41 Geurts, Pierre. 2001. "Pattern extraction for time series classification." In European Conference on Principles of Data Mining and Knowledge Discovery, 115-27. Springer. Glorot, Xavier, Antoine Bordes, and Yoshua Bengio. 2011. "Deep sparse rectifier neural networks." In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 315-23. Hatami, Nima, Yann Gavet, and Johan Debayle. 2018. "Classification of time-series images using deep convolutional neural networks." In Tenth International Conference on Machine Vision (ICMV 2017), 106960Y. International Society for Optics and Photonics. Hodgkin, Alan L, and Andrew F Huxley. 1952. 'A quantitative description of membrane current and its application to conduction and excitation in nerve', The Journal of physiology, 117: 500-44. Hubel, David H, and Torsten N Wiesel. 1968. 'Receptive fields and functional architecture of monkey striate cortex', The Journal of physiology, 195: 215-43. Islam, Faridul, Muhammad Shahbaz, Ashraf U Ahmed, and Md Mahmudul Alam. 2013. 'Financial development and energy consumption nexus in Malaysia: a multivariate time series analysis', Economic Modelling, 30: 435-41. LeCun, Yann, Bernhard E Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne E Hubbard, and Lawrence D Jackel. 1990. "Handwritten digit recognition with a back-propagation network." In Advances in neural information processing systems, 396-404. Li, Chuanjun, Latifur Khan, and Balakrishnan Prabhakaran. 2007. 'Feature selection for classification of variable length multiattribute motions.' in, Multimedia data mining and knowledge discovery (Springer). Łuczak, Maciej. 2018. 'Combining raw and normalized data in multivariate time series classification with dynamic time warping', Journal of Intelligent & Fuzzy Systems, 34: 373-80. Marwan, Norbert. 2005. 'Typical examples of recurrence plots', Wikipedia. https://en.wikipedia.org/wiki/Recurrence_plot. Mottahedi, Mohammad, and Somayeh Asadi. 'Non-intrusive Load Monitoring Using Imaging Time Series and Convolutional Neural Networks'. Muth, John F. 1960. 'Optimal properties of exponentially weighted forecasts', Journal of the american statistical association, 55: 299-306. Nagem, Tarek AM Hamad, Rami Qahwaji, and Stan Ipson. 2017. "Deep learning teachology for the prediction of solar flares from GOES data." In Computing Conference, 2017, 697-700. IEEE.
    42 Olszewski, Robert T. 2001. "Generalized feature extraction for structural pattern recognition in time-series data." In.: CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE. Prieto, Oscar J, Carlos J Alonso-González, and Juan J Rodríguez. 2015. 'Stacking for multivariate time series classification', Pattern Analysis and Applications, 18: 297-312. Silva, Diego F, Vinicius MA De Souza, and Gustavo EAPA Batista. 2013. "Time series classification using compression distance of recurrence plots." In Data Mining (ICDM), 2013 IEEE 13th International Conference on, 687-96. IEEE. Technical University of Denmark, DTU Informatics, E-mail: reception@imm.dtu.dk. 2012. 'Prediction as a candidate for learning deep hierarchical models of data'. Toshev, Alexander, and Christian Szegedy. 2014. "Deeppose: Human pose estimation via deep neural networks." In Proceedings of the IEEE conference on computer vision and pattern recognition, 1653-60. Wang, Zhiguang, and Tim Oates. 2015a. "Encoding time series as images for visual inspection and classification using tiled convolutional neural networks." In Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence, 40-46. ———. 2015b. 'Imaging time-series to improve classification and imputation', arXiv preprint arXiv:1506.00327. Weng, Xiaoqing, and Junyi Shen. 2008. 'Classification of multivariate time series using locality preserving projections', Knowledge-Based Systems, 21: 581-87. Yao, Yuan, Lorenzo Rosasco, and Andrea Caponnetto. 2007. 'On early stopping in gradient descent learning', Constructive Approximation, 26: 289-315. Yu, Guangliang, Xiang Li, Dawei Song, Xiaozhao Zhao, Peng Zhang, Yuexian Hou, and Bin Hu. 2016. "Encoding physiological signals as images for affective state recognition using convolutional neural networks." In Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the, 812-15. IEEE. Zheng, Yi, Qi Liu, Enhong Chen, Yong Ge, and J Leon Zhao. 2014. "Time series classification using multi-channels deep convolutional neural networks." In International Conference on Web-Age Information Management, 298-310. Springer. Zhou, Bolei, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. 2016. "Learning deep features for discriminative localization." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2921-29.

    無法下載圖示 全文公開日期 2023/07/25 (校內網路)
    全文公開日期 本全文未授權公開 (校外網路)
    全文公開日期 本全文未授權公開 (國家圖書館：臺灣博碩士論文系統)
    QR CODE