Multivariate Time Series Classification by Combining Trend-Based and Value-Based Approximations

详细信息    查看全文

• 作者：Bilal Esmael (1) Bilal@stud.unileoben.ac.at
  Arghad Arnaout (2) Arghad.Arnaout@tde.at
  Rudolf K. Fruhwirth (2) Rudolf.Fruhwirth@tde.at
  Gerhard Thonhauser (1) Gerhard.Thonhauser@unileoben.ac.at
• 关键词：Time Series Classification – ; Time Series Representation – ; Symbolic Aggregate Approximation – ; Event Detection
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7336
• 期：1
• 页码：392-403
• 全文大小：695.5 KB
• 参考文献：1. Ratanamahatana, C.A., Lin, J., Gunopulos, D., Keogh, E., Vlachos, M., Das, G.: In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook 2010, 2nd edn., pp. 1049–1077. Springer (2010)
  2. Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A Symbolic Representation of Time Series, with Implications for Streaming Algorithms. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, San Diego, CA, June 13 (2003)
  3. Batal, I., Valizadegan, H., Cooper, G.F., Hauskrecht, M.: A Pattern Mining Approach for Classifying Multivariate Temporal Data. In: IEEE International Conference on Bioinformatics and Biomedicine, Atlanta, Georgia (November 2011)
  4. Batal, I., Sacchi, L., Bellazzi, R., Hauskrecht, M.: Multivariate Time Series Classification with Temporal Abstractions. In: Proceedings of the Twenty-Second International Florida AI Research Society Conference (FLAIRS 2009) (May 2009)
  5. Onishi, A., Watanabe, C.: Event Detection using Archived Smart House Sensor Data obtained using Symbolic Aggregate Approximation. In: PDPTA (2011)
  6. Zoumboulakis, M., Roussos, G.: Escalation: Complex Event Detection in Wireless Sensor Networks. In: Kortuem, G., Finney, J., Lea, R., Sundramoorthy, V. (eds.) EuroSSC 2007. LNCS, vol. 4793, pp. 270–285. Springer, Heidelberg (2007)
  7. Wei, L., Keogh, E.: Semi-Supervised Time Series Classification. In: The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, SIGKDD (2006)
  8. Keogh, E., Pazzani, M.: An enhanced representation of time series which allows fast and accurate classification clustering and relevance feedback. In: 4th International Conference on Knowledge Discovery and Data Mining, New York, August 27-31, pp. 239–243 (1998)
  9. Hung, N.Q.V., Anh, D.T.: Combining SAX and Piecewise Linear Approximation to Improve Similarity Search on Financial Time Series. In: Proceedings of the 2007 IEEE International Symposium on Information Technology Convergence (ISITC 2007), Jeonju, Korea (2007)
  10. Kontaki, M., Papadopoulos, A.N., Manolopoulos, Y.: Continuous Trend-Based Classification of Streaming Time Series. In: Eder, J., Haav, H.-M., Kalja, A., Penjam, J. (eds.) ADBIS 2005. LNCS, vol. 3631, pp. 294–308. Springer, Heidelberg (2005)
  11. Keogh, E., Lin, J., Fu, A.: HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. In: Proceeding of the 5th IEEE International Conference on Data Mining (ICDM 2005), Houston, Texas, November 27-30, pp. 226–233 (2005)
  12. Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid Prototyping for Complex Data Mining Tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2006 (2006)
  13. Chih-Chung, C., Chih-Jen, L.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software, http://www.csie.ntu.edu.tw/~cjlin/libsvm
• 作者单位：1. University of Leoben, 8700 Leoben, Austria2. TDE GmbH, 8700 Leoben, Austria
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Multivariate time series data often have a very high dimensionality. Classifying such high dimensional data poses a challenge because a vast number of features can be extracted. Furthermore, the meaning of the normally intuitive term “similar to” needs to be precisely defined. Representing the time series data effectively is an essential task for decision-making activities such as prediction, clustering and classification. In this paper we propose a feature-based classification approach to classify real-world multivariate time series generated by drilling rig sensors in the oil and gas industry. Our approach encompasses two main phases: representation and classification.